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 | |
| Publication status | Published - 2018 Mar 1 |
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Signal Processing
- Computer Networks and Communications
- Applied Mathematics
Cite this
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Exploiting the categorical reliability difference for binary classification. / Sun, Lei; Toh, Kar Ann; Chen, Badong; Lin, Zhiping.
In: Journal of the Franklin Institute, Vol. 355, No. 4, 01.03.2018, p. 2022-2040.Research output: Contribution to journal › Article
TY - JOUR
T1 - Exploiting the categorical reliability difference for binary classification
AU - Sun, Lei
AU - Toh, Kar Ann
AU - Chen, Badong
AU - Lin, Zhiping
PY - 2018/3/1
Y1 - 2018/3/1
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.
UR - http://www.scopus.com/inward/record.url?scp=85038849332&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85038849332&partnerID=8YFLogxK
U2 - 10.1016/j.jfranklin.2017.11.024
DO - 10.1016/j.jfranklin.2017.11.024
M3 - Article
AN - SCOPUS:85038849332
VL - 355
SP - 2022
EP - 2040
JO - Journal of the Franklin Institute
JF - Journal of the Franklin Institute
SN - 0016-0032
IS - 4
ER -