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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-...

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Autores principales: Wang, Xiujuan, Zhou, Yuchen
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
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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.
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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
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