Cargando…
Multi-Label Feature Selection with Conditional Mutual Information
Feature selection is an important way to optimize the efficiency and accuracy of classifiers. However, traditional feature selection methods cannot work with many kinds of data in the real world, such as multi-label data. To overcome this challenge, multi-label feature selection is developed. Multi-...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9569236/ https://www.ncbi.nlm.nih.gov/pubmed/36254206 http://dx.doi.org/10.1155/2022/9243893 |
_version_ | 1784809817098944512 |
---|---|
author | Wang, Xiujuan Zhou, Yuchen |
author_facet | Wang, Xiujuan Zhou, Yuchen |
author_sort | Wang, Xiujuan |
collection | PubMed |
description | Feature selection is an important way to optimize the efficiency and accuracy of classifiers. However, traditional feature selection methods cannot work with many kinds of data in the real world, such as multi-label data. To overcome this challenge, multi-label feature selection is developed. Multi-label feature selection plays an irreplaceable role in pattern recognition and data mining. This process can improve the efficiency and accuracy of multi-label classification. However, traditional multi-label feature selection based on mutual information does not fully consider the effect of redundancy among labels. The deficiency may lead to repeated computing of mutual information and leave room to enhance the accuracy of multi-label feature selection. To deal with this challenge, this paper proposed a multi-label feature selection based on conditional mutual information among labels (CRMIL). Firstly, we analyze how to reduce the redundancy among features based on existing papers. Secondly, we propose a new approach to diminish the redundancy among labels. This method takes label sets as conditions to calculate the relevance between features and labels. This approach can weaken the impact of the redundancy among labels on feature selection results. Finally, we analyze this algorithm and balance the effects of relevance and redundancy on the evaluation function. For testing CRMIL, we compare it with the other eight multi-label feature selection algorithms on ten datasets and use four evaluation criteria to examine the results. Experimental results illustrate that CRMIL performs better than other existing algorithms. |
format | Online Article Text |
id | pubmed-9569236 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-95692362022-10-16 Multi-Label Feature Selection with Conditional Mutual Information Wang, Xiujuan Zhou, Yuchen Comput Intell Neurosci Research Article Feature selection is an important way to optimize the efficiency and accuracy of classifiers. However, traditional feature selection methods cannot work with many kinds of data in the real world, such as multi-label data. To overcome this challenge, multi-label feature selection is developed. Multi-label feature selection plays an irreplaceable role in pattern recognition and data mining. This process can improve the efficiency and accuracy of multi-label classification. However, traditional multi-label feature selection based on mutual information does not fully consider the effect of redundancy among labels. The deficiency may lead to repeated computing of mutual information and leave room to enhance the accuracy of multi-label feature selection. To deal with this challenge, this paper proposed a multi-label feature selection based on conditional mutual information among labels (CRMIL). Firstly, we analyze how to reduce the redundancy among features based on existing papers. Secondly, we propose a new approach to diminish the redundancy among labels. This method takes label sets as conditions to calculate the relevance between features and labels. This approach can weaken the impact of the redundancy among labels on feature selection results. Finally, we analyze this algorithm and balance the effects of relevance and redundancy on the evaluation function. For testing CRMIL, we compare it with the other eight multi-label feature selection algorithms on ten datasets and use four evaluation criteria to examine the results. Experimental results illustrate that CRMIL performs better than other existing algorithms. Hindawi 2022-10-08 /pmc/articles/PMC9569236/ /pubmed/36254206 http://dx.doi.org/10.1155/2022/9243893 Text en Copyright © 2022 Xiujuan Wang and Yuchen Zhou. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Wang, Xiujuan Zhou, Yuchen Multi-Label Feature Selection with Conditional Mutual Information |
title | Multi-Label Feature Selection with Conditional Mutual Information |
title_full | Multi-Label Feature Selection with Conditional Mutual Information |
title_fullStr | Multi-Label Feature Selection with Conditional Mutual Information |
title_full_unstemmed | Multi-Label Feature Selection with Conditional Mutual Information |
title_short | Multi-Label Feature Selection with Conditional Mutual Information |
title_sort | multi-label feature selection with conditional mutual information |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9569236/ https://www.ncbi.nlm.nih.gov/pubmed/36254206 http://dx.doi.org/10.1155/2022/9243893 |
work_keys_str_mv | AT wangxiujuan multilabelfeatureselectionwithconditionalmutualinformation AT zhouyuchen multilabelfeatureselectionwithconditionalmutualinformation |