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Shojaee Z, Shahzadeh Fazeli SA, Abbasi E, Adibnia F, Masuli F, Rovetta S. A Mutual Information Based on Ant Colony Optimization Method to Feature Selection for Categorical Data Clustering. IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY, TRANSACTIONS A: SCIENCE 2022. [DOI: 10.1007/s40995-022-01395-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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An Improved Multi-objective Particle Swarm Optimization with Mutual Information Feedback Model and Its Application. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022. [DOI: 10.1007/s13369-021-06178-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Rashno A, Shafipour M, Fadaei S. Particle ranking: An Efficient Method for Multi-Objective Particle Swarm Optimization Feature Selection. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108640] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Nagarajan G, Dhinesh Babu LD. A hybrid feature selection model based on improved squirrel search algorithm and rank aggregation using fuzzy techniques for biomedical data classification. ACTA ACUST UNITED AC 2021; 10:39. [PMID: 34094808 PMCID: PMC8170065 DOI: 10.1007/s13721-021-00313-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 04/30/2021] [Accepted: 05/06/2021] [Indexed: 11/29/2022]
Abstract
Feature selection has gained its importance due to the voluminous nature of the data. Owing to the computational complexity of wrapper approaches, the poor performance of filtering techniques, and the classifier dependency of embedded approaches, hybrid approaches are more commonly used in feature selection. Hybrid approaches use filtering metrics to reduce the computational complexity of wrapper algorithms and are proved to yield better feature subset. Though filtering metrics select the features based on their significance, most of them are unstable and biased towards the metric used. Moreover, the choice of filtering metrics depends largely on the distribution of data and data types. Biomedical datasets contain features with different distribution and types adding to the complexity in the choice of filtering metric. We address this problem by proposing a stable filtering method based on rank aggregation in hybrid feature selection model with Improved Squirrel search algorithm for biomedical datasets. Our proposed model is compared with other well-known and state-of-the-art methods and the results prove that our model exhibited superior performance in terms of classification accuracy and computational time. The robustness of our proposed model is proved by conducting experiments on nine biomedical datasets and with three different classifiers.
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Affiliation(s)
- Gayathri Nagarajan
- School of Information Technology and Engineering, VIT university, Vellore, India
| | - L. D. Dhinesh Babu
- School of Information Technology and Engineering, VIT university, Vellore, India
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Song Y, Ren M. A Novel Just-in-Time Learning Strategy for Soft Sensing with Improved Similarity Measure Based on Mutual Information and PLS. SENSORS 2020; 20:s20133804. [PMID: 32646027 PMCID: PMC7374429 DOI: 10.3390/s20133804] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 06/28/2020] [Accepted: 07/04/2020] [Indexed: 11/16/2022]
Abstract
In modern industrial process control, just-in-time learning (JITL)-based soft sensors have been widely applied. An accurate similarity measure is crucial in JITL-based soft sensor modeling since it is not only the basis for selecting the nearest neighbor samples but also determines sample weights. In recent years, JITL similarity measure methods have been greatly enriched, including methods based on Euclidean distance, weighted Euclidean distance, correlation, etc. However, due to the different influence of input variables on output, the complex nonlinear relationship between input and output, the collinearity between input variables, and other complex factors, the above similarity measure methods may become inaccurate. In this paper, a new similarity measure method is proposed by combining mutual information (MI) and partial least squares (PLS). A two-stage calculation framework, including a training stage and a prediction stage, was designed in this study to reduce the online computational burden. In the prediction stage, to establish the local model, an improved locally weighted PLS (LWPLS) with variables and samples double-weighted was adopted. The above operations constitute a novel JITL modeling strategy, which is named MI-PLS-LWPLS. By comparison with other related JITL methods, the effectiveness of the MI-PLS-LWPLS method was verified through case studies on both a synthetic Friedman dataset and a real industrial dataset.
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Affiliation(s)
- Yueli Song
- School of Management, Hefei University of Technology, Hefei 230009, China;
- Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei 230009, China
| | - Minglun Ren
- School of Management, Hefei University of Technology, Hefei 230009, China;
- Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei 230009, China
- Correspondence: ; Tel.: +86-139-0569-3529
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Zhong K, Han M, Qiu T, Han B. Fault Diagnosis of Complex Processes Using Sparse Kernel Local Fisher Discriminant Analysis. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020; 31:1581-1591. [PMID: 31265419 DOI: 10.1109/tnnls.2019.2920903] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
As an outstanding discriminant analysis technique, Fisher discriminant analysis (FDA) gained extensive attention in supervised dimensionality reduction and fault diagnosis fields. However, it typically ignores the multimodality within the measured data, which may cause infeasibility in practice. In addition, it generally incorporates all process variables without emphasizing the key faulty ones when modeling the complex process, thus leading to degraded fault classification capability and poor model interpretability. To ease the above two drawbacks of conventional FDA, this brief presents an advantageously sparse local FDA (SLFDA) model, it first preserves the within-class multimodality by introducing local weighting factors into scatter matrix. Then, the responsible faulty variables are identified automatically through the elastic net algorithm, and the current optimization problem is subsequently settled through the feasible gradient direction method. Since then, the local data structure characteristics are exploited from both the sample dimension and variable dimension so that the fault diagnosis performance and model interpretability are significantly enhanced. In addition, we naturally extend SLFDA model to nonlinear variant (i.e., sparse kernel local FDA) by the kernel trick, which is substantially more resistant to strong nonlinearity. The simulation studies on Tennessee Eastman (TE) benchmark process and real-world diesel engine working process both validate that the novel diagnosis strategy is more accurate and reliable than the existing state-of-the-art methods.
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Prediction of $${\mathrm{PM}}_{2.5}$$ concentration based on multi-source data and self-organizing fuzzy neural network. SN APPLIED SCIENCES 2020. [DOI: 10.1007/s42452-020-2380-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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Gu X, Guo J, Xiao L, Ming T, Li C. A Feature Selection Algorithm Based on Equal Interval Division and Minimal-Redundancy–Maximal-Relevance. Neural Process Lett 2019. [DOI: 10.1007/s11063-019-10144-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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A parallel rough set based dependency calculation method for efficient feature selection. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2017.10.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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12
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Javidi MM, Zarisfi Kermani F. Utilizing the advantages of both global and local search strategies for finding a small subset of features in a two-stage method. APPL INTELL 2018. [DOI: 10.1007/s10489-018-1159-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Feature selection using rough set-based direct dependency calculation by avoiding the positive region. Int J Approx Reason 2018. [DOI: 10.1016/j.ijar.2017.10.012] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Wang Y, Feng L, Li Y. Two-step based feature selection method for filtering redundant information. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017. [DOI: 10.3233/jifs-161541] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Youwei Wang
- School of Information, Central University of Finance and Economics, Beijing, China
| | - Lizhou Feng
- School of Science and Engineering, Tianjin University of Finance and Economics, Tianjin, China
| | - Yang Li
- School of Information, Central University of Finance and Economics, Beijing, China
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Wang Y, Feng L, Zhu J. Novel artificial bee colony based feature selection method for filtering redundant information. APPL INTELL 2017. [DOI: 10.1007/s10489-017-1010-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Multi-objective feature selection for warfarin dose prediction. Comput Biol Chem 2017; 69:126-133. [DOI: 10.1016/j.compbiolchem.2017.06.002] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2016] [Revised: 06/06/2017] [Accepted: 06/22/2017] [Indexed: 02/05/2023]
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Liu Y, Liu K, Zhang C, Wang J, Wang X. Unsupervised feature selection via Diversity-induced Self-representation. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.09.043] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Yao W, Zeng Z, Lian C. Generating probabilistic predictions using mean-variance estimation and echo state network. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.09.064] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Correlation Feature Selection and Mutual Information Theory Based Quantitative Research on Meteorological Impact Factors of Module Temperature for Solar Photovoltaic Systems. ENERGIES 2016. [DOI: 10.3390/en10010007] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Wang J, Liu F, Song Y, Zhao J. A novel model: Dynamic choice artificial neural network (DCANN) for an electricity price forecasting system. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2016.07.011] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Qiao J, Li S, Li W. Mutual information based weight initialization method for sigmoidal feedforward neural networks. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.05.054] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Effective Sensor Selection and Data Anomaly Detection for Condition Monitoring of Aircraft Engines. SENSORS 2016; 16:s16050623. [PMID: 27136561 PMCID: PMC4883314 DOI: 10.3390/s16050623] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2016] [Revised: 04/06/2016] [Accepted: 04/14/2016] [Indexed: 11/17/2022]
Abstract
In a complex system, condition monitoring (CM) can collect the system working status. The condition is mainly sensed by the pre-deployed sensors in/on the system. Most existing works study how to utilize the condition information to predict the upcoming anomalies, faults, or failures. There is also some research which focuses on the faults or anomalies of the sensing element (i.e., sensor) to enhance the system reliability. However, existing approaches ignore the correlation between sensor selecting strategy and data anomaly detection, which can also improve the system reliability. To address this issue, we study a new scheme which includes sensor selection strategy and data anomaly detection by utilizing information theory and Gaussian Process Regression (GPR). The sensors that are more appropriate for the system CM are first selected. Then, mutual information is utilized to weight the correlation among different sensors. The anomaly detection is carried out by using the correlation of sensor data. The sensor data sets that are utilized to carry out the evaluation are provided by National Aeronautics and Space Administration (NASA) Ames Research Center and have been used as Prognostics and Health Management (PHM) challenge data in 2008. By comparing the two different sensor selection strategies, the effectiveness of selection method on data anomaly detection is proved.
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