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Chen H, Tan C, Lin Z, Chen M, Cheng B. Applying virtual sample generation and ensemble modeling for improving the spectral diagnosis of cancer. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 318:124518. [PMID: 38796889 DOI: 10.1016/j.saa.2024.124518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 05/11/2024] [Accepted: 05/23/2024] [Indexed: 05/29/2024]
Abstract
Cancer diagnosis plays a key role in facilitating treatment and improving survival rates of patients. The combination of near-infrared (NIR) spectroscopy with data-driven algorithms offers a rapid and cost-effective approach for such a task. Due to the limitations of objective cases, the number of tumor samples is usually smaller, and the resulting dataset exhibit the issues of class imbalance, which has a more serious impact on the performance of diagnostic models. To deal with class imbalance and improve the sensitivity, this work investigates the feasibility of NIR spectroscopy combined with virtual sample generation (VSG) as well as ensemble strategy for developing diagnostic models. Based on preliminary experiment, several learning algorithms such as discriminant analysis (DA) and partial least square-discriminant analysis (PLS-DA) are screened out as algorithms for constructing prediction models. Three algorithms of VSG including synthetic minority oversampling technique (SMOTE), Borderline-SMOTE and adaptive synthetic sampling (ADASYN) are used for experiment. A fixed sample subset composed of 27 cancer samples and 54 normal samples are hold out as the test set. Three training sets containing 5, 10, 25 minority class samples and 54 majority class samples are used for model development. The experimental result indicates that overall, with PLS-DA algorithm, all VSG approaches can significantly improve the sensitivity of cancer diagnosis for all cases of training sets with different minority samples, but ADASYN performs the best. It reveals that the integration of NIR, PLS-DA, and ADASYN is a promising tool package for developing diagnosis methods.
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Affiliation(s)
- Hui Chen
- Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, China; Hospital, Yibin University, Yibin, Sichuan 644000, China
| | - Chao Tan
- Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, China; College of Materials and Chemical Engineering, Yibin University, Yibin, Sichuan 644000, China.
| | - Zan Lin
- Department of Knee Sports Injury, Sichuan Province Orthopedic Hospital, Chengdu, Sichuan 610041, China
| | - Maoxian Chen
- Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, China
| | - Bin Cheng
- Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, China
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Small-sample continual learning classification method with vaccine to update memory cells based on the artificial immune system. Biosystems 2022; 220:104737. [DOI: 10.1016/j.biosystems.2022.104737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 07/11/2022] [Accepted: 07/11/2022] [Indexed: 11/23/2022]
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Li L, Kumar Damarla S, Wang Y, Huang B. A Gaussian mixture model based virtual sample generation approach for small datasets in industrial processes. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.09.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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Zhang XH, Xu Y, He YL, Zhu QX. Novel manifold learning based virtual sample generation for optimizing soft sensor with small data. ISA TRANSACTIONS 2021; 109:229-241. [PMID: 33070985 DOI: 10.1016/j.isatra.2020.10.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 09/03/2020] [Accepted: 10/03/2020] [Indexed: 06/11/2023]
Abstract
Due to the extremely complex mechanism and strong non-linear characteristics of industrial processes, data-driven soft sensor technologies play a key role in the intelligent measurement of process industries. However, the information of the collected process data in the steady stage is quite limited and unreliable, causing the small sample problem. As a result, it becomes an intractable challenge to catch the nature of the process and build accurate soft sensor models. To solve this problem, this paper proposes a novel manifold learning based virtual sample generation method (Isomap-VSG) to generate feasible virtual samples in the information gaps for supplementing the original small sample space. To find data sparse regions reasonably, one kind of manifold learning methods called Isomap is used to visualize process data with high dimension. Then virtual samples can be generated by the interpolation method and extreme learning machine. The simulation results on a standard dataset and a real-world application demonstrate that, compared with other advanced methods, the proposed Isomap-VSG method can achieve better performance in terms of generating feasible virtual samples and improving the accuracy of soft sensor models using limited samples.
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Affiliation(s)
- Xiao-Han Zhang
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, 100029, China; Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
| | - Yuan Xu
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, 100029, China; Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
| | - Yan-Lin He
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, 100029, China; Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China.
| | - Qun-Xiong Zhu
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, 100029, China; Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China.
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Integrating virtual sample generation with input-training neural network for solving small sample size problems: application to purified terephthalic acid solvent system. Soft comput 2021. [DOI: 10.1007/s00500-021-05641-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Ossani PC, de Souza DC, Rossoni DF, Resende LV. Machine learning in classification and identification of nonconventional vegetables. J Food Sci 2020; 85:4194-4200. [PMID: 33174205 DOI: 10.1111/1750-3841.15514] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 10/01/2020] [Accepted: 10/09/2020] [Indexed: 12/01/2022]
Abstract
Vegetables are important in economic, social, and nutritional matters in both the Brazilian and international scenes. Hence, some researches have been carried out in order to encourage the production and consumption of different species such as nonconventional vegetables. These vegetables have an added value because of their nutritional quality and nostalgic appeal due to the reintroduction of these species. For this reason, this article proposes the use of the machine learning technique in the construction of models for supervised classification and identification in an experiment with five leafy special of nonconventional vegetables (Tropaeolum majus, Rumex acetosa, Stachys byzantina, Lactuca cf. indica e Pereskia aculeata) assessing the characteristics of the macro and micro nutrients. In order to evaluate the classifiers' performance, the cross-validation procedure via Monte Carlo simulation was considered to confirm the model. In ten replications, the success and error rates were obtained, considering the false positive and false negative rates, sensibility, and accuracy of the classification method. Thus, it was concluded that the use of machine learning is viable because it allows the classification and identification of nonconventional vegetables using few nutritional attributes and obtaining a success rate of over 89% in most of the classifiers tested.
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Affiliation(s)
- Paulo César Ossani
- Department of Statistics, State University of Maringá, Av. Colombo, 5790, Bloco E-90, University Campus, Maringá, Paraná, 87020-900, Brazil
| | - Douglas Correa de Souza
- Department of Agriculture, Federal University of Lavras, UFLA, University Campus, s/n, mailbox 3037, Lavras, MG, 37200-000, Brazil
| | - Diogo Francisco Rossoni
- Department of Statistics, State University of Maringá, Av. Colombo, 5790, Bloco E-90, University Campus, Maringá, Paraná, 87020-900, Brazil
| | - Luciane Vilela Resende
- Department of Agriculture, Federal University of Lavras, UFLA, University Campus, s/n, mailbox 3037, Lavras, MG, 37200-000, Brazil
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Optimized Mahalanobis-Taguchi System for High-Dimensional Small Sample Data Classification. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2020; 2020:4609423. [PMID: 32405295 PMCID: PMC7199641 DOI: 10.1155/2020/4609423] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Revised: 12/15/2019] [Accepted: 12/28/2019] [Indexed: 01/01/2023]
Abstract
The Mahalanobis–Taguchi system (MTS) is a multivariate data diagnosis and prediction technology, which is widely used to optimize large sample data or unbalanced data, but it is rarely used for high-dimensional small sample data. In this paper, the optimized MTS for the classification of high-dimensional small sample data is discussed from two aspects, namely, the inverse matrix instability of the covariance matrix and the instability of feature selection. Firstly, based on regularization and smoothing techniques, this paper proposes a modified Mahalanobis metric to calculate the Mahalanobis distance, which is aimed at reducing the influence of the inverse matrix instability under small sample conditions. Secondly, the minimum redundancy-maximum relevance (mRMR) algorithm is introduced into the MTS for the instability problem of feature selection. By using the mRMR algorithm and signal-to-noise ratio (SNR), a two-stage feature selection method is proposed: the mRMR algorithm is first used to remove noise and redundant variables; the orthogonal table and SNR are then used to screen the combination of variables that make great contribution to classification. Then, the feasibility and simplicity of the optimized MTS are shown in five datasets from the UCI database. The Mahalanobis distance based on regularization and smoothing techniques (RS-MD) is more robust than the traditional Mahalanobis distance. The two-stage feature selection method improves the effectiveness of feature selection for MTS. Finally, the optimized MTS is applied to email classification of the Spambase dataset. The results show that the optimized MTS outperforms the classical MTS and the other 3 machine learning algorithms.
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Liao Q, Ding Y, Jiang ZL, Wang X, Zhang C, Zhang Q. Multi-task deep convolutional neural network for cancer diagnosis. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2018.06.084] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Fischer D, Berro A, Nordhausen K, Ruiz-Gazen A. REPPlab: An R package for detecting clusters and outliers using exploratory projection pursuit. COMMUN STAT-SIMUL C 2019. [DOI: 10.1080/03610918.2019.1626880] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Daniel Fischer
- Applied Statistical Methods, Natural Resources Institute Finland (Luke), Jokioinen, Finland
| | - Alain Berro
- Institut de Recherche en Informatique de Toulouse, University of Toulouse Capitole, Toulouse, France
| | - Klaus Nordhausen
- Institute of Statistics & Mathematical Methods in Economics, Vienna University of Technology, Wien, Austria
| | - Anne Ruiz-Gazen
- Toulouse School of Economics, University of Toulouse Capitole, Toulouse, France
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Alanni R, Hou J, Azzawi H, Xiang Y. A novel gene selection algorithm for cancer classification using microarray datasets. BMC Med Genomics 2019; 12:10. [PMID: 30646919 PMCID: PMC6334429 DOI: 10.1186/s12920-018-0447-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 12/07/2018] [Indexed: 12/18/2022] Open
Abstract
Background Microarray datasets are an important medical diagnostic tool as they represent the states of a cell at the molecular level. Available microarray datasets for classifying cancer types generally have a fairly small sample size compared to the large number of genes involved. This fact is known as a curse of dimensionality, which is a challenging problem. Gene selection is a promising approach that addresses this problem and plays an important role in the development of efficient cancer classification due to the fact that only a small number of genes are related to the classification problem. Gene selection addresses many problems in microarray datasets such as reducing the number of irrelevant and noisy genes, and selecting the most related genes to improve the classification results. Methods An innovative Gene Selection Programming (GSP) method is proposed to select relevant genes for effective and efficient cancer classification. GSP is based on Gene Expression Programming (GEP) method with a new defined population initialization algorithm, a new fitness function definition, and improved mutation and recombination operators. . Support Vector Machine (SVM) with a linear kernel serves as a classifier of the GSP. Results Experimental results on ten microarray cancer datasets demonstrate that Gene Selection Programming (GSP) is effective and efficient in eliminating irrelevant and redundant genes/features from microarray datasets. The comprehensive evaluations and comparisons with other methods show that GSP gives a better compromise in terms of all three evaluation criteria, i.e., classification accuracy, number of selected genes, and computational cost. The gene set selected by GSP has shown its superior performances in cancer classification compared to those selected by the up-to-date representative gene selection methods. Conclusion Gene subset selected by GSP can achieve a higher classification accuracy with less processing time.
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Affiliation(s)
- Russul Alanni
- School of Information Technology, Deakin University, Burwood, 3125, VIC, Australia.
| | - Jingyu Hou
- School of Information Technology, Deakin University, Burwood, 3125, VIC, Australia
| | - Hasseeb Azzawi
- School of Information Technology, Deakin University, Burwood, 3125, VIC, Australia
| | - Yong Xiang
- School of Information Technology, Deakin University, Burwood, 3125, VIC, Australia
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Jia Z, Bian J, Wang Y. Impacts of urban land use on the spatial distribution of groundwater pollution, Harbin City, Northeast China. JOURNAL OF CONTAMINANT HYDROLOGY 2018; 215:29-38. [PMID: 30082036 DOI: 10.1016/j.jconhyd.2018.06.005] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Revised: 06/23/2018] [Accepted: 06/29/2018] [Indexed: 06/08/2023]
Abstract
Groundwater is the major available water resources in Harbin City, China, but rapid urbanization has adversely affected it in recent years and groundwater quality is deteriorating. In this study, the groundwater pollution indexes of groundwater monitoring wells were analyzed by using the projection pursuit dynamic clustering model and its spatial distribution was interpolated by using the modified local polynomial interpolation method. The "searchlight" shape model was used to extract the types of land use within the scope of each monitoring well in order to evaluate the relationship between the spatial distribution of groundwater pollution and land use. The correlation between the groundwater pollution indexes and land use was evaluated using Kendall's tau-b analysis. The groundwater pollution index is generally high. It has obvious spatial distribution characteristics and it mainly occurs in DaoW-NanG district and the eastern part of DongL-XiangF district. The results of Kendall's tau-b analysis show that the groundwater pollution indexes have a good correlation with land use. Residential and commercial areas, industrial area and agricultural area greatly influence the distribution of groundwater pollution. Moreover, the main impact indicators of groundwater pollution in the study area are NH4+, NO3- and CaCO3.
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Affiliation(s)
- Zhuo Jia
- Key Laboratory of Groundwater Resources and Environment of the Ministry of Education, College of Environment and Resources, Jilin University, 2519 Jiefang Road, Changchun 130021, PR China; College of Environment and Resources, Jilin University, Changchun 130021, China
| | - Jianmin Bian
- Key Laboratory of Groundwater Resources and Environment of the Ministry of Education, College of Environment and Resources, Jilin University, 2519 Jiefang Road, Changchun 130021, PR China; College of Environment and Resources, Jilin University, Changchun 130021, China.
| | - Yu Wang
- Key Laboratory of Groundwater Resources and Environment of the Ministry of Education, College of Environment and Resources, Jilin University, 2519 Jiefang Road, Changchun 130021, PR China; College of Environment and Resources, Jilin University, Changchun 130021, China
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Quantitative evaluation of A2O and reversed A2O processes for biological municipal wastewater treatment using a projection pursuit method. Sep Purif Technol 2016. [DOI: 10.1016/j.seppur.2016.04.036] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Ni S, Lv J, Cheng Z, Li M. Novel Online Dimensionality Reduction Method with Improved Topology Representing and Radial Basis Function Networks. PLoS One 2015; 10:e0131631. [PMID: 26161960 PMCID: PMC4498733 DOI: 10.1371/journal.pone.0131631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2015] [Accepted: 06/05/2015] [Indexed: 11/19/2022] Open
Abstract
This paper presents improvements to the conventional Topology Representing Network to build more appropriate topology relationships. Based on this improved Topology Representing Network, we propose a novel method for online dimensionality reduction that integrates the improved Topology Representing Network and Radial Basis Function Network. This method can find meaningful low-dimensional feature structures embedded in high-dimensional original data space, process nonlinear embedded manifolds, and map the new data online. Furthermore, this method can deal with large datasets for the benefit of improved Topology Representing Network. Experiments illustrate the effectiveness of the proposed method.
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Affiliation(s)
- Shengqiao Ni
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, P. R. China
| | - Jiancheng Lv
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, P. R. China
| | - Zhehao Cheng
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, P. R. China
| | - Mao Li
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, P. R. China
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