Feature Selection Based on Neighborhood Discrimination Index
Feature selection is viewed as an important preprocessing step for pattern recognition, machine learning, and data mining. Neighborhood is one of the most important concepts in classification learning and can be used to distinguish samples with different decisions. In this paper, a neighborhood disc...
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| Published in | IEEE transaction on neural networks and learning systems Vol. 29; no. 7; pp. 2986 - 2999 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.07.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2162-237X 2162-2388 2162-2388 |
| DOI | 10.1109/TNNLS.2017.2710422 |
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| Abstract | Feature selection is viewed as an important preprocessing step for pattern recognition, machine learning, and data mining. Neighborhood is one of the most important concepts in classification learning and can be used to distinguish samples with different decisions. In this paper, a neighborhood discrimination index is proposed to characterize the distinguishing information of a neighborhood relation. It reflects the distinguishing ability of a feature subset. The proposed discrimination index is computed by considering the cardinality of a neighborhood relation rather than neighborhood similarity classes. Variants of the discrimination index, including joint discrimination index, conditional discrimination index, and mutual discrimination index, are introduced to compute the change of distinguishing information caused by the combination of multiple feature subsets. They have the similar properties as Shannon entropy and its variants. A parameter, named neighborhood radius, is introduced in these discrimination measures to address the analysis of real-valued data. Based on the proposed discrimination measures, the significance measure of a candidate feature is defined and a greedy forward algorithm for feature selection is designed. Data sets selected from public data sources are used to compare the proposed algorithm with existing algorithms. The experimental results confirm that the discrimination index-based algorithm yields superior performance compared to other classical algorithms. |
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| AbstractList | Feature selection is viewed as an important preprocessing step for pattern recognition, machine learning, and data mining. Neighborhood is one of the most important concepts in classification learning and can be used to distinguish samples with different decisions. In this paper, a neighborhood discrimination index is proposed to characterize the distinguishing information of a neighborhood relation. It reflects the distinguishing ability of a feature subset. The proposed discrimination index is computed by considering the cardinality of a neighborhood relation rather than neighborhood similarity classes. Variants of the discrimination index, including joint discrimination index, conditional discrimination index, and mutual discrimination index, are introduced to compute the change of distinguishing information caused by the combination of multiple feature subsets. They have the similar properties as Shannon entropy and its variants. A parameter, named neighborhood radius, is introduced in these discrimination measures to address the analysis of real-valued data. Based on the proposed discrimination measures, the significance measure of a candidate feature is defined and a greedy forward algorithm for feature selection is designed. Data sets selected from public data sources are used to compare the proposed algorithm with existing algorithms. The experimental results confirm that the discrimination index-based algorithm yields superior performance compared to other classical algorithms. Feature selection is viewed as an important preprocessing step for pattern recognition, machine learning, and data mining. Neighborhood is one of the most important concepts in classification learning and can be used to distinguish samples with different decisions. In this paper, a neighborhood discrimination index is proposed to characterize the distinguishing information of a neighborhood relation. It reflects the distinguishing ability of a feature subset. The proposed discrimination index is computed by considering the cardinality of a neighborhood relation rather than neighborhood similarity classes. Variants of the discrimination index, including joint discrimination index, conditional discrimination index, and mutual discrimination index, are introduced to compute the change of distinguishing information caused by the combination of multiple feature subsets. They have the similar properties as Shannon entropy and its variants. A parameter, named neighborhood radius, is introduced in these discrimination measures to address the analysis of real-valued data. Based on the proposed discrimination measures, the significance measure of a candidate feature is defined and a greedy forward algorithm for feature selection is designed. Data sets selected from public data sources are used to compare the proposed algorithm with existing algorithms. The experimental results confirm that the discrimination index-based algorithm yields superior performance compared to other classical algorithms.Feature selection is viewed as an important preprocessing step for pattern recognition, machine learning, and data mining. Neighborhood is one of the most important concepts in classification learning and can be used to distinguish samples with different decisions. In this paper, a neighborhood discrimination index is proposed to characterize the distinguishing information of a neighborhood relation. It reflects the distinguishing ability of a feature subset. The proposed discrimination index is computed by considering the cardinality of a neighborhood relation rather than neighborhood similarity classes. Variants of the discrimination index, including joint discrimination index, conditional discrimination index, and mutual discrimination index, are introduced to compute the change of distinguishing information caused by the combination of multiple feature subsets. They have the similar properties as Shannon entropy and its variants. A parameter, named neighborhood radius, is introduced in these discrimination measures to address the analysis of real-valued data. Based on the proposed discrimination measures, the significance measure of a candidate feature is defined and a greedy forward algorithm for feature selection is designed. Data sets selected from public data sources are used to compare the proposed algorithm with existing algorithms. The experimental results confirm that the discrimination index-based algorithm yields superior performance compared to other classical algorithms. |
| Author | Wang, Xizhao Chen, Degang Qian, Yuhua Dong, Zhe Wang, Changzhong Hu, Qinghua |
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| References | ref57 ref13 ref56 ref12 ref59 ref15 ref58 ref14 ref52 ref55 ho (ref64) 2008; 30 ref17 xu (ref54) 2009; 9 ref16 ref19 ref18 wu (ref53) 2012; 115 pawlak (ref37) 1991 sun (ref44) 2010; 32 ref51 ref50 ref45 ref48 ref47 ref42 ref41 ref43 nie (ref35) 2010 ref49 ref8 (ref65) 2015 ref7 ref9 ref4 ref6 ref5 ref40 guyon (ref10) 2003; 3 ref34 ref36 ref31 ref30 ref33 ref32 ref2 ref1 ref39 ref38 torkkola (ref46) 2003; 3 ref24 ref23 ref26 ref25 ref20 ref63 ref22 ref21 ref28 ref27 ref29 blake (ref3) 1998 ref60 ref62 hall (ref11) 2000 ref61 |
| References_xml | – ident: ref22 doi: 10.1016/S0031-3203(00)00057-1 – ident: ref47 doi: 10.1109/TKDE.2011.67 – volume: 32 start-page: 1610 year: 2010 ident: ref44 article-title: Local-learning-based feature selection for high-dimensional data analysis publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2009.190 – volume: 115 start-page: 203 year: 2012 ident: ref53 article-title: Knowledge reduction in random incomplete decision tables via evidence theory publication-title: Fundam Inf – ident: ref13 doi: 10.1109/34.990132 – ident: ref21 doi: 10.1109/TFUZZ.2006.889761 – ident: ref57 doi: 10.1016/j.ins.2015.08.011 – ident: ref24 doi: 10.1109/72.977291 – ident: ref26 doi: 10.1109/TNNLS.2015.2469100 – year: 1998 ident: ref3 publication-title: UCI repository of machine learning databases – ident: ref60 doi: 10.1109/TKDE.2009.118 – volume: 30 start-page: 1557 year: 2008 ident: ref64 article-title: Query by transduction publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2007.70811 – ident: ref23 doi: 10.1109/TPAMI.2002.1114861 – ident: ref9 doi: 10.1002/int.10014 – ident: ref39 doi: 10.1109/TPAMI.2005.159 – ident: ref63 doi: 10.1016/j.ins.2013.06.012 – ident: ref6 doi: 10.1109/TSMCB.2012.2228480 – ident: ref28 doi: 10.1109/TKDE.2005.66 – ident: ref27 doi: 10.1109/TKDE.2012.146 – ident: ref19 doi: 10.1016/j.ijar.2009.02.003 – ident: ref56 doi: 10.1016/S0020-0255(98)10006-3 – ident: ref58 doi: 10.1080/03081079208945039 – ident: ref30 doi: 10.1080/03081070512331318329 – ident: ref59 doi: 10.1109/TFUZZ.2012.2231417 – ident: ref29 doi: 10.1016/j.ins.2008.03.013 – ident: ref4 doi: 10.1109/TFUZZ.2011.2173695 – ident: ref51 doi: 10.1109/TPAMI.2006.126 – volume: 9 start-page: 1244 year: 2009 ident: ref54 article-title: Knowledge granulation, knowledge entropy and knowledge uncertainty measure in ordered information systems publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2009.03.007 – start-page: 1813 year: 2010 ident: ref35 article-title: Efficient and robust feature selection via joint $\ell 2$ , 1-norms minimization publication-title: Proc Adv Neural Inf Process Syst (NIPS) – ident: ref18 doi: 10.1016/S0165-0114(01)00143-9 – ident: ref55 doi: 10.1109/TNNLS.2012.2212721 – ident: ref16 doi: 10.1016/j.eswa.2011.01.023 – ident: ref5 doi: 10.1016/S0004-3702(03)00079-1 – ident: ref45 doi: 10.1109/TNNLS.2015.2424721 – ident: ref32 doi: 10.1109/34.990133 – year: 1991 ident: ref37 publication-title: Rough Sets Theoretical Aspects of Reasoning about Data – ident: ref20 doi: 10.1109/TKDE.2004.96 – start-page: 359 year: 2000 ident: ref11 article-title: Correlation-based feature selection for discrete and numeric class machine learning publication-title: Proc 17th Int Conf Mach Learn – ident: ref48 doi: 10.1016/j.knosys.2016.08.009 – ident: ref49 doi: 10.1109/TFUZZ.2016.2574918 – ident: ref25 doi: 10.1016/j.knosys.2015.07.024 – ident: ref2 doi: 10.1016/S0020-0255(98)00019-X – ident: ref14 doi: 10.1109/TNNLS.2015.2461552 – ident: ref62 doi: 10.1016/j.ins.2012.04.018 – ident: ref34 doi: 10.1109/TNNLS.2015.2504382 – ident: ref1 doi: 10.1109/72.298224 – volume: 3 start-page: 1157 year: 2003 ident: ref10 article-title: An introduction to variable and feature selection publication-title: J Mach Learn Res – ident: ref12 doi: 10.1109/TNNLS.2013.2241788 – volume: 3 start-page: 1415 year: 2003 ident: ref46 article-title: Feature extraction by non-parametric mutual information maximization publication-title: J Mach Learn Res – ident: ref36 doi: 10.1109/TPAMI.2004.105 – ident: ref17 doi: 10.1109/TFUZZ.2005.864086 – ident: ref41 doi: 10.1109/TNNLS.2015.2451151 – ident: ref61 doi: 10.1016/j.ins.2008.03.022 – ident: ref43 doi: 10.1007/s13042-013-0206-0 – ident: ref7 doi: 10.1016/S0004-3702(98)00091-5 – ident: ref50 doi: 10.1109/TKDE.2015.2426703 – ident: ref40 doi: 10.1142/S0218488508005121 – ident: ref33 doi: 10.1109/TSMCB.2005.854499 – year: 2015 ident: ref65 publication-title: Kent Ridge Bio-medical Dataset – ident: ref38 doi: 10.1109/TKDE.2009.119 – ident: ref52 doi: 10.1016/S0020-0255(02)00180-9 – ident: ref15 doi: 10.1016/j.ins.2008.05.024 – ident: ref31 doi: 10.1016/j.ins.2003.07.004 – ident: ref8 doi: 10.1145/1015330.1015352 – ident: ref42 doi: 10.1109/69.842271 |
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| SubjectTerms | Algorithm design and analysis Algorithms Data mining Data processing Discrimination Discrimination index distinguishing information Entropy Entropy (Information theory) feature selection Greedy algorithms Indexes Learning algorithms Machine learning Manganese Mutual information neighborhood relation Neighborhoods Pattern recognition Preprocessing Uncertainty |
| Title | Feature Selection Based on Neighborhood Discrimination Index |
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