Exploiting the categorical reliability difference for binary classification
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 t...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/145211 |
| _version_ | 1826119959034462208 |
|---|---|
| author | Sun, Lei Toh, Kar-Ann Chen, Badong Lin, Zhiping |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Sun, Lei Toh, Kar-Ann Chen, Badong Lin, Zhiping |
| author_sort | Sun, Lei |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T05:08:40Z |
| format | Journal Article |
| id | ntu-10356/145211 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:08:40Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1452112020-12-15T04:48:08Z Exploiting the categorical reliability difference for binary classification Sun, Lei Toh, Kar-Ann Chen, Badong Lin, Zhiping School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Binary Classification Classification Algorithm 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. 2020-12-15T04:48:08Z 2020-12-15T04:48:08Z 2018 Journal Article Sun, L., Toh, K.-A., Chen, B., & Lin, Z. (2018). Exploiting the categorical reliability difference for binary classification. Journal of the Franklin Institute, 355(4), 2022-2040. doi:10.1016/j.jfranklin.2017.11.024 0016-0032 https://hdl.handle.net/10356/145211 10.1016/j.jfranklin.2017.11.024 4 355 2022 2040 en Journal of the Franklin Institute © 2017 The Franklin Institute. Published by Elsevier Ltd. All rights reserved. |
| spellingShingle | Engineering::Electrical and electronic engineering Binary Classification Classification Algorithm Sun, Lei Toh, Kar-Ann Chen, Badong Lin, Zhiping Exploiting the categorical reliability difference for binary classification |
| title | Exploiting the categorical reliability difference for binary classification |
| title_full | Exploiting the categorical reliability difference for binary classification |
| title_fullStr | Exploiting the categorical reliability difference for binary classification |
| title_full_unstemmed | Exploiting the categorical reliability difference for binary classification |
| title_short | Exploiting the categorical reliability difference for binary classification |
| title_sort | exploiting the categorical reliability difference for binary classification |
| topic | Engineering::Electrical and electronic engineering Binary Classification Classification Algorithm |
| url | https://hdl.handle.net/10356/145211 |
| work_keys_str_mv | AT sunlei exploitingthecategoricalreliabilitydifferenceforbinaryclassification AT tohkarann exploitingthecategoricalreliabilitydifferenceforbinaryclassification AT chenbadong exploitingthecategoricalreliabilitydifferenceforbinaryclassification AT linzhiping exploitingthecategoricalreliabilitydifferenceforbinaryclassification |