Exploiting the categorical reliability difference for binary classification

Lei Sun; Kar Ann Toh; Badong Chen; Zhiping Lin

doi:10.1016/j.jfranklin.2017.11.024

Exploiting the categorical reliability difference for binary classification

Lei Sun, Kar Ann Toh, Badong Chen, Zhiping Lin

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

In binary pattern classification, the reliabilities of statistics obtained from the samples of the two categories are generally different. When the statistics are used for modeling a classifier, such reliability difference could impact the generalization performance. We formulate a disparity index to show the statistical disparity based on the generalized eigenvalue decomposition of the categorical moment matrices. It is shown that this disparity index can effectively indicate the reliability difference between the two categories. The obtained reliability difference is subsequently utilized to adjust the regularization term of a classifier for effective learning generalization. Our experiments based on 10 real-world benchmark data sets validate the effectiveness of the proposed method.

Original language	English
Pages (from-to)	2022-2040
Number of pages	19
Journal	Journal of the Franklin Institute
Volume	355
Issue number	4
DOIs	https://doi.org/10.1016/j.jfranklin.2017.11.024
Publication status	Published - 2018 Mar

Bibliographical note

Funding Information:
This work was partly supported by National NSF of China (Nos. 61673059 , 61372152 and 91648208 ) and the National Basic Research Program of China (973 Program) under grant No. 2015CB351703.

Publisher Copyright:
© 2017 The Franklin Institute

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Signal Processing
Computer Networks and Communications
Applied Mathematics

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10.1016/j.jfranklin.2017.11.024

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title = "Exploiting the categorical reliability difference for binary classification",

abstract = "In binary pattern classification, the reliabilities of statistics obtained from the samples of the two categories are generally different. When the statistics are used for modeling a classifier, such reliability difference could impact the generalization performance. We formulate a disparity index to show the statistical disparity based on the generalized eigenvalue decomposition of the categorical moment matrices. It is shown that this disparity index can effectively indicate the reliability difference between the two categories. The obtained reliability difference is subsequently utilized to adjust the regularization term of a classifier for effective learning generalization. Our experiments based on 10 real-world benchmark data sets validate the effectiveness of the proposed method.",

author = "Lei Sun and Toh, {Kar Ann} and Badong Chen and Zhiping Lin",

note = "Funding Information: This work was partly supported by National NSF of China (Nos. 61673059 , 61372152 and 91648208 ) and the National Basic Research Program of China (973 Program) under grant No. 2015CB351703. Publisher Copyright: {\textcopyright} 2017 The Franklin Institute",

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AU - Sun, Lei

AU - Toh, Kar Ann

AU - Chen, Badong

AU - Lin, Zhiping

N1 - Funding Information: This work was partly supported by National NSF of China (Nos. 61673059 , 61372152 and 91648208 ) and the National Basic Research Program of China (973 Program) under grant No. 2015CB351703. Publisher Copyright: © 2017 The Franklin Institute

PY - 2018/3

Y1 - 2018/3

N2 - In binary pattern classification, the reliabilities of statistics obtained from the samples of the two categories are generally different. When the statistics are used for modeling a classifier, such reliability difference could impact the generalization performance. We formulate a disparity index to show the statistical disparity based on the generalized eigenvalue decomposition of the categorical moment matrices. It is shown that this disparity index can effectively indicate the reliability difference between the two categories. The obtained reliability difference is subsequently utilized to adjust the regularization term of a classifier for effective learning generalization. Our experiments based on 10 real-world benchmark data sets validate the effectiveness of the proposed method.

AB - In binary pattern classification, the reliabilities of statistics obtained from the samples of the two categories are generally different. When the statistics are used for modeling a classifier, such reliability difference could impact the generalization performance. We formulate a disparity index to show the statistical disparity based on the generalized eigenvalue decomposition of the categorical moment matrices. It is shown that this disparity index can effectively indicate the reliability difference between the two categories. The obtained reliability difference is subsequently utilized to adjust the regularization term of a classifier for effective learning generalization. Our experiments based on 10 real-world benchmark data sets validate the effectiveness of the proposed method.

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Exploiting the categorical reliability difference for binary classification

Abstract

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