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Savitha R, Suresh S, Sundararajan N. Fast learning complex-valued classifiers for real-valued classification problems. INT J MACH LEARN CYB 2012. [DOI: 10.1007/s13042-012-0112-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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252
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Sugavaneswaran L, Umapathy K, Krishnan S. Ambiguity domain-based identification of altered gait pattern in ALS disorder. J Neural Eng 2012; 9:046004. [DOI: 10.1088/1741-2560/9/4/046004] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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253
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Liu CL, Hsaio WH, Lee CH, Lu GC, Jou E. Movie Rating and Review Summarization in Mobile Environment. ACTA ACUST UNITED AC 2012. [DOI: 10.1109/tsmcc.2011.2136334] [Citation(s) in RCA: 73] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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254
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Amaral JLM, Lopes AJ, Jansen JM, Faria ACD, Melo PL. Machine learning algorithms and forced oscillation measurements applied to the automatic identification of chronic obstructive pulmonary disease. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2012; 105:183-93. [PMID: 22018532 DOI: 10.1016/j.cmpb.2011.09.009] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2011] [Revised: 08/15/2011] [Accepted: 09/22/2011] [Indexed: 05/02/2023]
Abstract
The purpose of this study is to develop a clinical decision support system based on machine learning (ML) algorithms to help the diagnostic of chronic obstructive pulmonary disease (COPD) using forced oscillation (FO) measurements. To this end, the performances of classification algorithms based on Linear Bayes Normal Classifier, K nearest neighbor (KNN), decision trees, artificial neural networks (ANN) and support vector machines (SVM) were compared in order to the search for the best classifier. Four feature selection methods were also used in order to identify a reduced set of the most relevant parameters. The available dataset consists of 7 possible input features (FO parameters) of 150 measurements made in 50 volunteers (COPD, n = 25; healthy, n = 25). The performance of the classifiers and reduced data sets were evaluated by the determination of sensitivity (Se), specificity (Sp) and area under the ROC curve (AUC). Among the studied classifiers, KNN, SVM and ANN classifiers were the most adequate, reaching values that allow a very accurate clinical diagnosis (Se > 87%, Sp > 94%, and AUC > 0.95). The use of the analysis of correlation as a ranking index of the FOT parameters, allowed us to simplify the analysis of the FOT parameters, while still maintaining a high degree of accuracy. In conclusion, the results of this study indicate that the proposed classifiers may contribute to easy the diagnostic of COPD by using forced oscillation measurements.
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Affiliation(s)
- Jorge L M Amaral
- Department of Electronics and Telecommunications Engineering, State University of Rio de Janeiro, Rio de Janeiro, Brazil
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255
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Learning algorithm and hidden node selection scheme for local coupled feedforward neural network classifier. Neurocomputing 2012. [DOI: 10.1016/j.neucom.2011.09.019] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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256
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Miao Y, Rooney D, Gan Q. Artificial Neural Network for Compositional Ionic Liquid Viscosity Prediction. INT J COMPUT INT SYS 2012. [DOI: 10.1080/18756891.2012.696909] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
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257
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Nanni L, Lumini A. Ensemble of Neural Networks for Automated Cell Phenotype Image Classification. Mach Learn 2012. [DOI: 10.4018/978-1-60960-818-7.ch405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Subcellular location is related to the knowledge of the spatial distribution of a protein within the cell. The knowledge of the location of all proteins is crucial for several applications ranging from early diagnosis of a disease to monitoring of therapeutic effectiveness of drugs. This chapter focuses on the study of machine learning techniques for cell phenotype image classification and is aimed at pointing out some of the advantages of using a multi-classifier system instead of a stand-alone method to solve this difficult classification problem. The main problems and solutions proposed in this field are discussed and a new approach is proposed based on ensemble of neural networks trained by local and global features. Finally, the most used benchmarks for this problem are presented and an experimental comparison among several state-of-the-art approaches is reported which allows to quantify the performance improvement obtained by the approach proposed in this chapter.
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258
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Nahar J, Tickle KS, Shawkat Ali AB. Pattern Discovery from Biological Data. Mach Learn 2012. [DOI: 10.4018/978-1-60960-818-7.ch403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Extracting useful information from structured and unstructured biological data is crucial in the health industry. Some examples include medical practitioner’s need to identify breast cancer patient in the early stage, estimate survival time of a heart disease patient, or recognize uncommon disease characteristics which suddenly appear. Currently there is an explosion in biological data available in the data bases. But information extraction and true open access to data are require time to resolve issues such as ethical clearance. The emergence of novel IT technologies allows health practitioners to facilitate the comprehensive analyses of medical images, genomes, transcriptomes, and proteomes in health and disease. The information that is extracted from such technologies may soon exert a dramatic change in the pace of medical research and impact considerably on the care of patients. The current research will review the existing technologies being used in heart and cancer research. Finally this research will provide some possible solutions to overcome the limitations of existing technologies. In summary the primary objective of this research is to investigate how existing modern machine learning techniques (with their strength and limitations) are being used in the indent of heartbeat related disease and the early detection of cancer in patients. After an extensive literature review these are the objectives chosen: to develop a new approach to find the association between diseases such as high blood pressure, stroke and heartbeat, to propose an improved feature selection method to analyze huge images and microarray databases for machine learning algorithms in cancer research, to find an automatic distance function selection method for clustering tasks, to discover the most significant risk factors for specific cancers, and to determine the preventive factors for specific cancers that are aligned with the most significant risk factors. Therefore we propose a research plan to attain these objectives within this chapter. The possible solutions of the above objectives are: new heartbeat identification techniques show promising association with the heartbeat patterns and diseases, sensitivity based feature selection methods will be applied to early cancer patient classification, meta learning approaches will be adopted in clustering algorithms to select an automatic distance function, and Apriori algorithm will be applied to discover the significant risks and preventive factors for specific cancers. We expect this research will add significant contributions to the medical professional to enable more accurate diagnosis and better patient care. It will also contribute in other area such as biomedical modeling, medical image analysis and early diseases warning.
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259
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Chohra A, Kanaoui N, Amarger V, Madani K. Hybrid Intelligent Diagnosis Approach Based On Neural Pattern Recognition and Fuzzy Decision-Making. Mach Learn 2012. [DOI: 10.4018/978-1-60960-818-7.ch307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Fault diagnosis is a complex and fuzzy cognitive process, and soft computing methods and technologies based on Neural Networks (NN) and Fuzzy Logic (FL), have shown great potential in the development of Decision Support Systems (DSS). Dealing with expert (human) knowledge consideration, Computer Aided Diagnosis (CAD) dilemma is one of the most interesting, but also one of the most difficult problems. Among difficulties contributing to challenging nature of this problem, one can mention the need of fine pattern recognition (classification) and decision-making. This Chapter deals with classification and decision-making based on Artificial Intelligence using multiple model approaches under soft computing implying modular Neural Networks (NN) and Fuzzy Logic (FL) for biomedical and industrial applications. The aim of this Chapter is absolutely not to replace specialized human but to suggest decision support tools: hybrid intelligent diagnosis systems with a satisfactory reliability degree for CAD. In this Chapter, a methodology is given in order to design hybrid intelligent diagnosis systems for a large field of biomedical and industrial applications. For this purpose, first, a survey on diagnosis tasks in such applications is presented. Second, fault diagnosis systems are presented. Third, the main steps of hybrid intelligent diagnosis systems are developed, for each step emphasizing problems and suggesting solutions able to ensure the design of hybrid intelligent diagnosis systems with a satisfactory reliability degree. In fact, the main steps discussed are knowledge representation, classification, classifier issued information fusion, and decision-making. Then, the suggested approach is developed for a CAD in biomedicine, from Auditory Brainstem Response (ABR) test, and the prototype design and experimental results are presented. Finally, a discussion is given with regard to the reliability and large application field of the suggested approach.
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260
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261
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Neuro-Fuzzy Quantification of Personal Perceptions of Facial Images Based on a Limited Data Set. ACTA ACUST UNITED AC 2011; 22:2422-34. [DOI: 10.1109/tnn.2011.2176349] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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262
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SHYU MEILING, CHEN CHAO, CHEN SHUCHING. MULTI-CLASS CLASSIFICATION VIA SUBSPACE MODELING. INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING 2011. [DOI: 10.1142/s1793351x1100116x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Aiming to build a satisfactory supervised classifier, this paper proposes a Multi-class Subspace Modeling (MSM) classification framework. The framework consists of three parts, namely Principal Component Classifier Training Array, Principal Component Classifier Testing Array, and Label Coordinator. The role of Principal Component Classifier Training Array is to get a set of optimized parameters and principal components from each subspace-based training classifier and pass them to the corresponding subspace-based testing classifier in Principal Component Classifier Testing Array. In each subspace-based training classifier, the instances are projected from the original space into the principal component (PC) subspace, where a PC selection method is developed and applied to construct the PC subspace. In Principal Component Classifier Testing Array, each subspace-based testing classifier will utilize the parameters and PCs from its corresponding subspace-based training classifier to determine whether to assign its class label to the instances. Since one instance may be assigned zero or more than one label by the Principal Component Classifier Testing Array, the Label Coordinator is designed to coordinate the final class label of an instance according to its Attaching Proportion (AP) values towards multiple classes. To evaluate the classification accuracy, 10 rounds of 3-fold cross-validation are conducted and many popular classification algorithms (like SVM, Decision Trees, Multi-layer Perceptron, Logistic, etc.) are served as comparative peers. Experimental results show that our proposed MSM classification framework outperforms those compared classifiers in 10 data sets, among which 8 of them hold a confidence level of significance higher than 99.5%. In addition, our framework shows its ability of handling imbalanced data set. Finally, a demo is built to display the accuracy and detailed information of the classification.
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Affiliation(s)
- MEI-LING SHYU
- Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL 33124, USA
| | - CHAO CHEN
- Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL 33124, USA
| | - SHU-CHING CHEN
- Distributed Multimedia Information Systems Laboratory, School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USA
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263
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Rady HAK. Shannon Entropy and Mean Square Errors for speeding the convergence of Multilayer Neural Networks: A comparative approach. EGYPTIAN INFORMATICS JOURNAL 2011. [DOI: 10.1016/j.eij.2011.09.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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264
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Tatt Hee Oong, Isa NAM. Adaptive Evolutionary Artificial Neural Networks for Pattern Classification. ACTA ACUST UNITED AC 2011; 22:1823-36. [DOI: 10.1109/tnn.2011.2169426] [Citation(s) in RCA: 67] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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265
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Issac Niwas S, Palanisamy P, Chibbar R, Zhang WJ. An expert support system for breast cancer diagnosis using color wavelet features. J Med Syst 2011; 36:3091-102. [PMID: 22005900 DOI: 10.1007/s10916-011-9788-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2011] [Accepted: 09/29/2011] [Indexed: 01/21/2023]
Abstract
Breast cancer diagnosis can be done through the pathologic assessments of breast tissue samples such as core needle biopsy technique. The result of analysis on this sample by pathologist is crucial for breast cancer patient. In this paper, nucleus of tissue samples are investigated after decomposition by means of the Log-Gabor wavelet on HSV color domain and an algorithm is developed to compute the color wavelet features. These features are used for breast cancer diagnosis using Support Vector Machine (SVM) classifier algorithm. The ability of properly trained SVM is to correctly classify patterns and make them particularly suitable for use in an expert system that aids in the diagnosis of cancer tissue samples. The results are compared with other multivariate classifiers such as Naïves Bayes classifier and Artificial Neural Network. The overall accuracy of the proposed method using SVM classifier will be further useful for automation in cancer diagnosis.
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Affiliation(s)
- S Issac Niwas
- Department of Electronics and Communication Engineering, National Institute of Technology, Tiruchirappalli, India.
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266
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Validation of a novel traditional chinese medicine pulse diagnostic model using an artificial neural network. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2011; 2012:685094. [PMID: 21918652 PMCID: PMC3171770 DOI: 10.1155/2012/685094] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/03/2011] [Revised: 06/27/2011] [Accepted: 07/12/2011] [Indexed: 11/30/2022]
Abstract
In view of lacking a quantifiable traditional Chinese medicine (TCM) pulse
diagnostic model, a novel TCM pulse diagnostic model was introduced to quantify
the pulse diagnosis. Content validation was performed with a panel of TCM
doctors. Criterion validation was tested with essential hypertension. The gold
standard was brachial blood pressure measured by a sphygmomanometer. Two hundred
and sixty subjects were recruited (139 in the normotensive group and 121 in the
hypertensive group). A TCM doctor palpated pulses at left and right cun, guan,
and chi points, and quantified pulse qualities according to eight elements
(depth, rate, regularity, width, length, smoothness, stiffness, and strength) on
a visual analog scale. An artificial neural network was used to develop a pulse
diagnostic model differentiating essential hypertension from normotension.
Accuracy, specificity, and sensitivity were compared among various diagnostic
models. About 80% accuracy was attained among all models. Their specificity and
sensitivity varied, ranging from 70% to nearly 90%. It suggested that the novel
TCM pulse diagnostic model was valid in terms of its content and diagnostic
ability.
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267
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Shin JH, Smith D, Swiercz W, Staley K, Rickard JT, Montero J, Kurgan LA, Cios KJ. Recognition of partially occluded and rotated images with a network of spiking neurons. ACTA ACUST UNITED AC 2011; 21:1697-709. [PMID: 21047704 DOI: 10.1109/tnn.2010.2050600] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In this paper, we introduce a novel system for recognition of partially occluded and rotated images. The system is based on a hierarchical network of integrate-and-fire spiking neurons with random synaptic connections and a novel organization process. The network generates integrated output sequences that are used for image classification. The proposed network is shown to provide satisfactory predictive performance given that the number of the recognition neurons and synaptic connections are adjusted to the size of the input image. Comparison of synaptic plasticity activity rule (SAPR) and spike timing dependant plasticity rules, which are used to learn connections between the spiking neurons, indicates that the former gives better results and thus the SAPR rule is used. Test results show that the proposed network performs better than a recognition system based on support vector machines.
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Affiliation(s)
- Joo-Heon Shin
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284 USA.
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268
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Zhang H, Yang J, Tao W, Zhao H. Vision method of inspecting missing fastening components in high-speed railway. APPLIED OPTICS 2011; 50:3658-3665. [PMID: 21743579 DOI: 10.1364/ao.50.003658] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
The increasing train speed on railways generates an urgent need for more powerful automatic inspection of railway tracks, including real-time fastening component inspection. To obtain better high-speed performance with lower cost, this paper has proposed a novel structured light method based on motion image (SLMMI) for moving object inspection. The motion images in the proposed method are insensitive to motion, abundant with information, and easy to process, resulting in a low performance requirement of the hardware. Compared to the conventional unstructured light method and structured light method, the proposed method inherits the virtues of both thus offering a fresh perspective when inspecting missing fastening components on high-speed railways. By using the SLMMI and the recognition method based on a neural network, the experimental results yield good performance in terms of speed and accuracy. Furthermore, the robustness of the proposed method is also discussed and simulated by adding typical interferences, such as ambient light, vibration, and obstacles.
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Affiliation(s)
- Haibo Zhang
- Department of Instrument Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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269
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Eksteen S, Breetzke GD. Predicting the abundance of African horse sickness vectors in South Africa using GIS and artificial neural networks. S AFR J SCI 2011. [DOI: 10.4102/sajs.v107i7/8.404] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022] Open
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270
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Yaghini M, Shadmani MA. GOFAM: a hybrid neural network classifier combining fuzzy ARTMAP and genetic algorithm. Artif Intell Rev 2011. [DOI: 10.1007/s10462-011-9265-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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271
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272
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Wang P, Weise T, Chiong R. Novel evolutionary algorithms for supervised classification problems: an experimental study. EVOLUTIONARY INTELLIGENCE 2011. [DOI: 10.1007/s12065-010-0047-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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273
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Memetic pareto differential evolutionary artificial neural networks to determine growth multi-classes in predictive microbiology. EVOLUTIONARY INTELLIGENCE 2010. [DOI: 10.1007/s12065-010-0045-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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274
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Westreich D, Lessler J, Funk MJ. Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. J Clin Epidemiol 2010; 63:826-33. [PMID: 20630332 DOI: 10.1016/j.jclinepi.2009.11.020] [Citation(s) in RCA: 230] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2009] [Revised: 10/07/2009] [Accepted: 11/15/2009] [Indexed: 12/20/2022]
Abstract
OBJECTIVE Propensity scores for the analysis of observational data are typically estimated using logistic regression. Our objective in this review was to assess machine learning alternatives to logistic regression, which may accomplish the same goals but with fewer assumptions or greater accuracy. STUDY DESIGN AND SETTING We identified alternative methods for propensity score estimation and/or classification from the public health, biostatistics, discrete mathematics, and computer science literature, and evaluated these algorithms for applicability to the problem of propensity score estimation, potential advantages over logistic regression, and ease of use. RESULTS We identified four techniques as alternatives to logistic regression: neural networks, support vector machines, decision trees (classification and regression trees [CART]), and meta-classifiers (in particular, boosting). CONCLUSION Although the assumptions of logistic regression are well understood, those assumptions are frequently ignored. All four alternatives have advantages and disadvantages compared with logistic regression. Boosting (meta-classifiers) and, to a lesser extent, decision trees (particularly CART), appear to be most promising for use in the context of propensity score analysis, but extensive simulation studies are needed to establish their utility in practice.
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Affiliation(s)
- Daniel Westreich
- Department of Epidemiology, University of North Carolina at Chapel Hill, NC 27599-7435, USA.
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275
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Diagnosing Breast Masses in Digital Mammography Using Feature Selection and Ensemble Methods. J Med Syst 2010; 36:569-77. [DOI: 10.1007/s10916-010-9518-8] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2010] [Accepted: 04/23/2010] [Indexed: 11/25/2022]
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276
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Fernandez Caballero JC, Martinez FJ, Hervas C, Gutierrez PA. Sensitivity Versus Accuracy in Multiclass Problems Using Memetic Pareto Evolutionary Neural Networks. ACTA ACUST UNITED AC 2010; 21:750-70. [DOI: 10.1109/tnn.2010.2041468] [Citation(s) in RCA: 120] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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277
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A neural network based retrainable framework for robust object recognition with application to mobile robotics. APPL INTELL 2010. [DOI: 10.1007/s10489-010-0212-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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278
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Marcos JV, Hornero R, Álvarez D, Nabney IT, del Campo F, Zamarrón C. The classification of oximetry signals using Bayesian neural networks to assist in the detection of obstructive sleep apnoea syndrome. Physiol Meas 2010; 31:375-94. [DOI: 10.1088/0967-3334/31/3/007] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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279
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Amaral JLM, Faria ACD, Lopes AJ, Jansen JM, Melo PL. Automatic identification of Chronic Obstructive Pulmonary Disease Based on forced oscillation measurements and artificial neural networks. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2010; 2010:1394-1397. [PMID: 21096340 DOI: 10.1109/iembs.2010.5626727] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
The purpose of this study is to develop an automatic classifier based on Artificial Neural Networks (ANNs) to help the diagnostic of Chronic Obstructive Pulmonary Disease (COPD) using forced oscillation measurements (FOT). The classifier inputs are the parameters provided by the FOT and the output is the indication if the parameters indicate COPD or not. The available dataset consists of 7 possible input features (FOT parameters) of 90 measurements made in 30 volunteers. Two feature selection methods (the analysis of the linear correlation and forward search) were used in order to identify a reduced set of the most relevant parameters. Two different training strategies for the ANNs were used and the performance of resulting networks were evaluated by the determination of accuracy, sensitivity (Se), specificity (Sp) and AUC. The ANN classifiers presented high accuracy (Se > 0.9, Se > 0.9 and AUC > 0.9) both in the complete and the reduce sets of FOT parameters. This indicates that ANNs classifiers may contribute to easy the diagnostic of COPD using forced oscillation measurements.
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Affiliation(s)
- Jorge L M Amaral
- Dept. of Electronics and Telecommunications Engineering, Rio de Janeiro State University, 20550-013, RJ, Brazil.
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280
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Chinchuluun A, Xanthopoulos P, Tomaino V, Pardalos P. Data Mining Techniques in Agricultural and Environmental Sciences. INTERNATIONAL JOURNAL OF AGRICULTURAL AND ENVIRONMENTAL INFORMATION SYSTEMS 2010. [DOI: 10.4018/jaeis.2010101302] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Data mining techniques are largely used in different sectors of the economy and they increasingly are playing an important role in agriculture and environment-related areas. This paper aims to show our vision on the importance of knowing and efficiently using data mining and machine learning-related techniques for knowledge discovery in the field of agriculture and environment. Efforts for searching hidden patterns in data are not a recent phenomenon. History shows that extensive observations on data have helped discover empirical laws in different fields of research. Therefore, it is important to provide researchers in agriculture and environmental-related areas with the most advanced knowledge discovery techniques. Data mining is the process of extracting important and useful information from large sets of data. This information can be converted into useful knowledge that could help to better understand the problem in study and to better predict future developments. The paper presents the state of the art in data mining and knowledge discovery techniques and provides discussions for future directions.
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Affiliation(s)
| | | | - Vera Tomaino
- University of Florida, USA and University Magna Græcia of Catanzaro, Italy
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281
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ACO-based hybrid classification system with feature subset selection and model parameters optimization. Neurocomputing 2009. [DOI: 10.1016/j.neucom.2009.07.014] [Citation(s) in RCA: 79] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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282
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Barrat S, Tabbone S. A Bayesian network for combining descriptors: application to symbol recognition. INT J DOC ANAL RECOG 2009. [DOI: 10.1007/s10032-009-0103-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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283
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A hybrid genetic based functional link artificial neural network with a statistical comparison of classifiers over multiple datasets. Neural Comput Appl 2009. [DOI: 10.1007/s00521-009-0310-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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284
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Sciarabba M, Serrao G, Bauer D, Arnaboldi F, Borghese N. Automatic detection of neurons in large cortical slices. J Neurosci Methods 2009; 182:123-40. [DOI: 10.1016/j.jneumeth.2009.05.021] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2009] [Revised: 05/14/2009] [Accepted: 05/27/2009] [Indexed: 11/26/2022]
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285
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Marcos JV, Hornero R, Alvarez D, del Campo F, Zamarrón C. Assessment of four statistical pattern recognition techniques to assist in obstructive sleep apnoea diagnosis from nocturnal oximetry. Med Eng Phys 2009; 31:971-8. [PMID: 19592290 DOI: 10.1016/j.medengphy.2009.05.010] [Citation(s) in RCA: 55] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2009] [Accepted: 05/31/2009] [Indexed: 11/28/2022]
Abstract
The aim of this study is to assess the utility of traditional statistical pattern recognition techniques to help in obstructive sleep apnoea (OSA) diagnosis. Classifiers based on quadratic (QDA) and linear (LDA) discriminant analysis, K-nearest neighbours (KNN) and logistic regression (LR) were evaluated. Spectral and nonlinear input features from oxygen saturation (SaO(2)) signals were applied. A total of 187 recordings from patients suspected of suffering from OSA were available. This initial dataset was divided into training set (74 subjects) and test set (113 subjects). Twelve classification algorithms were developed by applying QDA, LDA, KNN and LR with spectral features, nonlinear features and combination of both groups. The performance of each algorithm was measured on the test set by means of classification accuracy and receiver operating characteristic (ROC) analysis. QDA, LDA and LR showed better classification capability than KNN. The classifier based on LDA with spectral features provided the best diagnostic ability with an accuracy of 87.61% (91.05% sensitivity and 82.61% specificity) and an area under the ROC curve (AROC) of 0.925. The proposed statistical pattern recognition techniques could be applied as an OSA screening tool.
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Affiliation(s)
- J Víctor Marcos
- Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, Universidad de Valladolid, Camino del Cementerio s/n, 47011 Valladolid, Spain.
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286
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Dehuri S, Cho SB. A comprehensive survey on functional link neural networks and an adaptive PSO–BP learning for CFLNN. Neural Comput Appl 2009. [DOI: 10.1007/s00521-009-0288-5] [Citation(s) in RCA: 110] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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287
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Selver MA, Akay O, Ardali E, Yavuz AB, Onal O, Ozden G. Cascaded and Hierarchical Neural Networks for Classifying Surface Images of Marble Slabs. ACTA ACUST UNITED AC 2009. [DOI: 10.1109/tsmcc.2009.2013816] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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288
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Using a heuristic approach to derive a grey-box model through an artificial neural network knowledge extraction technique. Neural Comput Appl 2009. [DOI: 10.1007/s00521-009-0270-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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289
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290
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Memetic Pareto Differential Evolution for Designing Artificial Neural Networks in Multiclassification Problems Using Cross-Entropy Versus Sensitivity. LECTURE NOTES IN COMPUTER SCIENCE 2009. [DOI: 10.1007/978-3-642-02319-4_52] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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291
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A novel approach to neuro-fuzzy classification. Neural Netw 2009; 22:100-9. [DOI: 10.1016/j.neunet.2008.09.011] [Citation(s) in RCA: 42] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2006] [Revised: 09/18/2008] [Accepted: 09/18/2008] [Indexed: 11/21/2022]
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292
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Marcos JV, Hornero R, Alvarez D, Del Campo F, Zamarrón C, López M. Utility of multilayer perceptron neural network classifiers in the diagnosis of the obstructive sleep apnoea syndrome from nocturnal oximetry. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2008; 92:79-89. [PMID: 18672313 DOI: 10.1016/j.cmpb.2008.05.006] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2007] [Revised: 05/20/2008] [Accepted: 05/24/2008] [Indexed: 05/26/2023]
Abstract
The aim of this study is to assess the ability of multilayer perceptron (MLP) neural networks as an assistant tool in the diagnosis of the obstructive sleep apnoea syndrome (OSAS). Non-linear features from nocturnal oxygen saturation (SaO(2)) recordings were used to discriminate between OSAS positive and negative patients. A total of 187 subjects suspected of suffering from OSAS (111 with a positive diagnosis of OSAS and 76 with a negative diagnosis of OSAS) took part in the study. The initial population was divided into training, validation and test sets for deriving and testing our neural network classifier. Three methods were applied to extract non-linear features from SaO(2) signals: approximate entropy (ApEn), central tendency measure (CTM) and Lempel-Ziv complexity (LZC). The selected MLP-based classifier provided a diagnostic accuracy of 85.5% (89.8% sensitivity and 79.4% specificity). Our neural network algorithm could represent a useful technique for OSAS detection. It could contribute to reduce the demand for polysomnographic studies in OSAS screening.
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Affiliation(s)
- J Víctor Marcos
- Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Spain.
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293
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Huang CL, Dun JF. A distributed PSO–SVM hybrid system with feature selection and parameter optimization. Appl Soft Comput 2008. [DOI: 10.1016/j.asoc.2007.10.007] [Citation(s) in RCA: 250] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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294
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da Silva CG. Time series forecasting with a non-linear model and the scatter search meta-heuristic. Inf Sci (N Y) 2008. [DOI: 10.1016/j.ins.2008.03.024] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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295
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Moussa A, Sbihi A, Postaire JG. A Markov random field model for mode detection in cluster analysis. Pattern Recognit Lett 2008. [DOI: 10.1016/j.patrec.2008.01.033] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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296
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Abstract
This letter presents a minimum classification error learning formulation for a single-layer feedforward network (SLFN). By approximating the nonlinear counting step function using a quadratic function, the classification error rate is shown to be deterministically solvable. Essentially the derived solution is related to an existing weighted least-squares method with class-specific weights set according to the size of data set. By considering the class-specific weights as adjustable parameters, the learning formulation extends the classification robustness of the SLFN without sacrificing its intrinsic advantage of being a closed-form algorithm. While the method is applicable to other linear formulations, our empirical results indicate SLFN's effectiveness on classification generalization.
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Affiliation(s)
- Kar-Ann Toh
- Biometrics Engineering Research Center, School of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, Korea,
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297
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Marcos JV, Hornero R, Alvarez D, Del Campo F, López M. Applying neural network classifiers in the diagnosis of the obstructive sleep apnea syndrome from nocturnal pulse oximetric recordings. ACTA ACUST UNITED AC 2008; 2007:5174-7. [PMID: 18003173 DOI: 10.1109/iembs.2007.4353507] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The aim of this study was to assess the ability of neural networks as an assistant tool for the diagnosis of the obstructive sleep apnea syndrome (OSAS). A total of 187 subjects suspected of suffering from OSAS (111 with a positive diagnosis of OSAS and 76 with a negative diagnosis of OSAS) took part in the study. The initial population was divided into training, validation and test sets for deriving and testing our neural classifiers. Our method was based on spectral and non-linear features extracted from overnight arterial oxygen saturation (SaO_(2)) recordings. A seven-element input vector was used for patient classification. We selected four spectral features from the estimated power spectral density (PSD) of SaO_(2). In addition, three input features were computed from non-linear analysis of SaO_(2). Two neural classifiers were assessed: the multilayer perceptron (MLP) network and the radial basis function (RBF) network. The RBF classifier provided the best diagnostic performance with an accuracy of 86.3% (89.9% sensitivity and 81.1% specificity).
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Affiliation(s)
- J Victor Marcos
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, Camino del Cementerio s/n, 47011-Valladolid, Spain.
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298
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Mazurowski MA, Habas PA, Zurada JM, Lo JY, Baker JA, Tourassi GD. Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance. Neural Netw 2008; 21:427-36. [PMID: 18272329 PMCID: PMC2346433 DOI: 10.1016/j.neunet.2007.12.031] [Citation(s) in RCA: 229] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2007] [Revised: 12/02/2007] [Accepted: 12/11/2007] [Indexed: 12/16/2022]
Abstract
This study investigates the effect of class imbalance in training data when developing neural network classifiers for computer-aided medical diagnosis. The investigation is performed in the presence of other characteristics that are typical among medical data, namely small training sample size, large number of features, and correlations between features. Two methods of neural network training are explored: classical backpropagation (BP) and particle swarm optimization (PSO) with clinically relevant training criteria. An experimental study is performed using simulated data and the conclusions are further validated on real clinical data for breast cancer diagnosis. The results show that classifier performance deteriorates with even modest class imbalance in the training data. Further, it is shown that BP is generally preferable over PSO for imbalanced training data especially with small data sample and large number of features. Finally, it is shown that there is no clear preference between oversampling and no compensation approach and some guidance is provided regarding a proper selection.
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Affiliation(s)
- Maciej A Mazurowski
- Computational Intelligence Lab, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY 40292, USA.
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299
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300
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Cooperative recurrent modular neural networks for constrained optimization: a survey of models and applications. Cogn Neurodyn 2008; 3:47-81. [PMID: 19003467 DOI: 10.1007/s11571-008-9036-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2007] [Accepted: 11/27/2007] [Indexed: 10/22/2022] Open
Abstract
Constrained optimization problems arise in a wide variety of scientific and engineering applications. Since several single recurrent neural networks when applied to solve constrained optimization problems for real-time engineering applications have shown some limitations, cooperative recurrent neural network approaches have been developed to overcome drawbacks of these single recurrent neural networks. This paper surveys in details work on cooperative recurrent neural networks for solving constrained optimization problems and their engineering applications, and points out their standing models from viewpoint of both convergence to the optimal solution and model complexity. We provide examples and comparisons to shown advantages of these models in the given applications.
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