Feature selection for binary classification based on class labeling, SOM, and hierarchical clustering
Feature selection plays an important role in algorithms for processing high-dimensional data. Traditional pattern classification and information theory methods are widely applied to feature selection methods. However, traditional pattern classification methods such as Fisher Score, Laplacian Score,...
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| Vydané v: | Measurement and control (London) Ročník 56; číslo 9-10; s. 1649 - 1669 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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London, England
SAGE Publications
01.11.2023
Sage Publications Ltd SAGE Publishing |
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| ISSN: | 0020-2940, 2051-8730 |
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| Abstract | Feature selection plays an important role in algorithms for processing high-dimensional data. Traditional pattern classification and information theory methods are widely applied to feature selection methods. However, traditional pattern classification methods such as Fisher Score, Laplacian Score, and relief use class labels inadequately. Previous information theory based feature selection methods such as MIFS ignore the intra-class to tight inter-class to sparse property of the samples. To address these problems, a feature selection algorithm for the binary classification problem is proposed, which is based on class label transformation using self-organizing mapping neural network (SOM) and cohesive hierarchical clustering. The algorithm first converts class labels without numerical meaning into numerical values that can participate in operations and retain classification information through class label mapping, and constitutes a two-dimensional vector from it and the attribute values to be judged. Then, these two-dimensional vectors are clustered by using SOM neural network and hierarchical clustering. Finally, evaluation function value is calculated, that is closely related to intra-cluster to tightness, inter-cluster separation, and division accuracy after clustering, and is used to evaluate the ability of alternative attributes to distinguish between classes. It is experimentally verified that the algorithm is robust and can effectively screen attributes with strong classification ability and improve the prediction performance of the classifier. |
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| AbstractList | Feature selection plays an important role in algorithms for processing high-dimensional data. Traditional pattern classification and information theory methods are widely applied to feature selection methods. However, traditional pattern classification methods such as Fisher Score, Laplacian Score, and relief use class labels inadequately. Previous information theory based feature selection methods such as MIFS ignore the intra-class to tight inter-class to sparse property of the samples. To address these problems, a feature selection algorithm for the binary classification problem is proposed, which is based on class label transformation using self-organizing mapping neural network (SOM) and cohesive hierarchical clustering. The algorithm first converts class labels without numerical meaning into numerical values that can participate in operations and retain classification information through class label mapping, and constitutes a two-dimensional vector from it and the attribute values to be judged. Then, these two-dimensional vectors are clustered by using SOM neural network and hierarchical clustering. Finally, evaluation function value is calculated, that is closely related to intra-cluster to tightness, inter-cluster separation, and division accuracy after clustering, and is used to evaluate the ability of alternative attributes to distinguish between classes. It is experimentally verified that the algorithm is robust and can effectively screen attributes with strong classification ability and improve the prediction performance of the classifier. |
| Author | Zhengtian, Zhao Zhiyuan, Rui Xiaoyan, Duan |
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| Cites_doi | 10.1109/TNSRE.2013.2293575 10.1073/pnas.87.23.9193 10.1109/TKDE.2005.66 10.1016/j.jbi.2018.07.014 10.1109/72.80236 10.1007/s10115-012-0487-8 10.1109/5.58325 10.1016/S0004-3702(96)00034-3 10.1109/TNN.2008.2005601 10.1016/j.patcog.2015.11.007 10.1109/ACCESS.2022.3205618 10.1109/ACCESS.2020.2981265 10.1109/TPAMI.2005.159 10.3233/IDA-1997-1302 10.3389/fbinf.2022.927312 10.1109/TIP.2020.3011253 10.1109/72.298224 10.1109/TII.2008.2002920 10.1109/TCYB.2020.3018815 10.1109/TPAMI.2020.3002843 |
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| Keywords | SOM hierarchical clustering Feature selection class labeling |
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| References | Wolberg, Mangasarian 1990; 87 Guyon, Elisseeff 2003; 3 Dietterich, Lathrop, Lozano-Pérez 1997; 89 Roffo, Melzi, Castellani 2021; 43 Islam, Lima, Das 2022; 10 Urbanowicz, Meeker, Cava 2018; 85 Kohonen 1990; 78 Haro-García, Toledano, Cerruela-García 2022; 52 Chen, Ma, Huang 2012; 49 Tsanas, Little, Fox 2014; 22 Peng, Long, Ding 2005; 27 Pudjihartono, Fadason, Kempa-Liehr 2022; 2 Yu 2009 Bolón-Canedo, Sánchez-Maroño, Alonso-Betanzos 2013; 34 Henni, Mezghani, Mitiche 2020; 8 Le, Alex, Arthur 2012; 13 Kamin 1990; 1 Vinh, Zhou, Chan 2016; 53 Gong, Huang, Chen 2008; 4 Brown, Pocock, Zhao 2012; 13 Fleuret 2004; 5 Dash, Liu 1997; 1 Zhang, Li 2020; 29 Estévez, Tesmer, Perez 2009; 20 Battiti 1994; 5 Liu, Yu 2005; 17 Zhao Z (bibr11-00202940231173748) bibr18-00202940231173748 bibr5-00202940231173748 Brown G (bibr24-00202940231173748) 2012; 13 bibr12-00202940231173748 Haykin S (bibr26-00202940231173748) 2008 bibr7-00202940231173748 Yu Y (bibr28-00202940231173748) 2009 Fleuret F (bibr21-00202940231173748) 2004; 5 bibr32-00202940231173748 bibr22-00202940231173748 bibr8-00202940231173748 bibr25-00202940231173748 Jović A (bibr4-00202940231173748) Le S (bibr13-00202940231173748) bibr9-00202940231173748 Nie F (bibr15-00202940231173748) bibr27-00202940231173748 bibr1-00202940231173748 Langley P (bibr2-00202940231173748) bibr31-00202940231173748 bibr17-00202940231173748 bibr34-00202940231173748 Chen T (bibr33-00202940231173748) 2012; 49 Guyon I (bibr3-00202940231173748) 2003; 3 He X (bibr10-00202940231173748) bibr23-00202940231173748 bibr16-00202940231173748 Le S (bibr14-00202940231173748) 2012; 13 bibr29-00202940231173748 bibr19-00202940231173748 bibr20-00202940231173748 bibr30-00202940231173748 bibr6-00202940231173748 |
| References_xml | – volume: 43 start-page: 4396 year: 2021 end-page: 4410 article-title: Infinite feature selection: a graph-based feature filtering approach publication-title: IEEE Trans Pattern Anal Mach Intell – volume: 2 start-page: 1 year: 2022 end-page: 17 article-title: A review of feature selection methods for machine learning-based disease risk prediction publication-title: Front Bioinform – volume: 5 start-page: 537 year: 1994 end-page: 550 article-title: Using mutual information for selecting features in supervised neural net learning publication-title: IEEE Trans Neural Netw – volume: 13 start-page: 27 year: 2012 end-page: 66 article-title: Conditional likelihood maximisation: A unifying framework for information theoretic feature selection publication-title: J Mach Learn Res – volume: 10 start-page: 99595 year: 2022 end-page: 99632 article-title: A comprehensive survey on the process, methods, evaluation, and challenges of feature selection publication-title: IEEE Access – volume: 27 start-page: 1226 year: 2005 end-page: 1238 article-title: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy publication-title: IEEE Trans Pattern Anal Mach Intell – volume: 29 start-page: 8097 year: 2020 end-page: 8106 article-title: Unsupervised feature selection via data reconstruction and side information publication-title: IEEE Trans Image Process – volume: 52 start-page: 2942 year: 2022 end-page: 2954 article-title: Grab’Em: a novel graph-based method for combining feature subset selectors publication-title: IEEE Trans Cybern – volume: 8 start-page: 62841 year: 2020 end-page: 62854 article-title: Cluster density properties define a graph for effective pattern feature selection publication-title: IEEE Access – volume: 20 start-page: 189 year: 2009 end-page: 201 article-title: Normalized mutual information feature selection publication-title: IEEE Trans Neural Netw – volume: 1 start-page: 239 year: 1990 end-page: 242 article-title: A simple procedure for pruning back-propagation trained neural networks publication-title: IEEE Trans Neural Netw – volume: 4 start-page: 198 year: 2008 end-page: 206 article-title: Robust and efficient rule extraction through data summarization and its application in welding fault diagnosis publication-title: IEEE Trans Ind Inform – volume: 87 start-page: 9193 year: 1990 end-page: 9196 article-title: Multisurface method of pattern separation for medical diagnosis applied to breast cytology publication-title: Proc Natl Acad Sci USA – volume: 78 start-page: 1464 year: 1990 end-page: 1480 article-title: The self-organization map publication-title: Proc IEEE – volume: 13 start-page: 1393 year: 2012 end-page: 1434 article-title: Feature selection via dependence maximization publication-title: J Mach Learn Res – volume: 17 start-page: 491 year: 2005 end-page: 502 article-title: Toward integrating feature selection algorithms for classification and clustering publication-title: IEEE Trans Knowl Data Eng – volume: 22 start-page: 181 year: 2014 end-page: 190 article-title: Objective automatic assessment of rehabilitative speech treatment in Parkinson’s disease publication-title: IEEE Trans Neural Syst Rehabil Eng – volume: 85 start-page: 189 year: 2018 end-page: 203 article-title: Relief-based feature selection: introduction and review publication-title: J Biomed Inform – volume: 5 start-page: 1531 year: 2004 end-page: 1555 article-title: Fast binary feature selection with conditional mutual information publication-title: J Mach Learn Res – volume: 1 start-page: 131 year: 1997 end-page: 156 article-title: Feature selection for classification publication-title: Intell Data Anal – year: 2009 publication-title: A study of clustering and data analysis methods based on one-dimensional SOM – volume: 3 start-page: 1157 year: 2003 end-page: 1182 article-title: An introduction to variable and feature selection publication-title: J Mach Learn Res – volume: 49 start-page: 735 year: 2012 end-page: 745 article-title: Novel and efficient method on feature selection and data classification publication-title: J Comput Res Dev – volume: 89 start-page: 31 year: 1997 end-page: 71 article-title: Solving the multiple instance problem with axis-parallel rectangles publication-title: Artif Intell – volume: 53 start-page: 46 year: 2016 end-page: 58 article-title: Can high-order dependencies improve mutual information based feature selection? publication-title: Pattern Recognit – volume: 34 start-page: 483 year: 2013 end-page: 519 article-title: A review of feature selection methods on synthetic data publication-title: Knowl Inf Syst – volume: 5 start-page: 1531 year: 2004 ident: bibr21-00202940231173748 publication-title: J Mach Learn Res – ident: bibr32-00202940231173748 doi: 10.1109/TNSRE.2013.2293575 – ident: bibr30-00202940231173748 doi: 10.1073/pnas.87.23.9193 – ident: bibr1-00202940231173748 doi: 10.1109/TKDE.2005.66 – volume: 3 start-page: 1157 year: 2003 ident: bibr3-00202940231173748 publication-title: J Mach Learn Res – start-page: 507 volume-title: Proceedings of the 18th international conference on neural information processing systems (NIPS’05) ident: bibr10-00202940231173748 – ident: bibr12-00202940231173748 doi: 10.1016/j.jbi.2018.07.014 – ident: bibr29-00202940231173748 doi: 10.1109/72.80236 – start-page: 127 volume-title: Proceedings of the AAAI fall symposium on relevance ident: bibr2-00202940231173748 – volume: 49 start-page: 735 year: 2012 ident: bibr33-00202940231173748 publication-title: J Comput Res Dev – ident: bibr8-00202940231173748 doi: 10.1007/s10115-012-0487-8 – ident: bibr27-00202940231173748 doi: 10.1109/5.58325 – ident: bibr31-00202940231173748 doi: 10.1016/S0004-3702(96)00034-3 – ident: bibr23-00202940231173748 doi: 10.1109/TNN.2008.2005601 – ident: bibr25-00202940231173748 doi: 10.1016/j.patcog.2015.11.007 – ident: bibr6-00202940231173748 doi: 10.1109/ACCESS.2022.3205618 – volume: 13 start-page: 1393 year: 2012 ident: bibr14-00202940231173748 publication-title: J Mach Learn Res – ident: bibr16-00202940231173748 doi: 10.1109/ACCESS.2020.2981265 – ident: bibr22-00202940231173748 doi: 10.1109/TPAMI.2005.159 – ident: bibr7-00202940231173748 doi: 10.3233/IDA-1997-1302 – start-page: 1200 volume-title: International convention on information and communication technology, Electronics and Microelectronics (MIPRO) ident: bibr4-00202940231173748 – ident: bibr5-00202940231173748 doi: 10.3389/fbinf.2022.927312 – volume: 13 start-page: 27 year: 2012 ident: bibr24-00202940231173748 publication-title: J Mach Learn Res – start-page: 671 volume-title: Proceedings of the 23rd national conference on Artificial intelligence ident: bibr15-00202940231173748 – ident: bibr17-00202940231173748 doi: 10.1109/TIP.2020.3011253 – ident: bibr20-00202940231173748 doi: 10.1109/72.298224 – volume-title: Neural networks and learning machines year: 2008 ident: bibr26-00202940231173748 – start-page: 1151 volume-title: Proceedings of the 24th international conference on machine learning (ICML’07) ident: bibr11-00202940231173748 – ident: bibr34-00202940231173748 doi: 10.1109/TII.2008.2002920 – ident: bibr19-00202940231173748 doi: 10.1109/TCYB.2020.3018815 – year: 2009 ident: bibr28-00202940231173748 publication-title: A study of clustering and data analysis methods based on one-dimensional SOM – start-page: 823 volume-title: Proceedings of the 24th international conference on machine learning (ICML’07) ident: bibr13-00202940231173748 – ident: bibr18-00202940231173748 doi: 10.1109/TPAMI.2020.3002843 – ident: bibr9-00202940231173748 |
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| SubjectTerms | Algorithms Classification Cluster analysis Clustering Feature selection Information theory Labels Mapping Neural networks Pattern classification Robustness (mathematics) Tightness |
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| Title | Feature selection for binary classification based on class labeling, SOM, and hierarchical clustering |
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