Weighted Decoding for the Competence Reliability Problem of ECOC Multiclass Classification

Error-Correcting Output Codes has become a well-known, established technique for multiclass classification due to its simplicity and efficiency. Each binary split contains different original classes. A noncompetent classifier emerges when it classifies an instance whose real class does not belong to...

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Autores principales:	Lei Lei, Yafei Song
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/5cc7a797b47745e3b28950314b19db4f
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spelling	oai:doaj.org-article:5cc7a797b47745e3b28950314b19db4f2021-11-08T02:36:24ZWeighted Decoding for the Competence Reliability Problem of ECOC Multiclass Classification1687-527310.1155/2021/5583031https://doaj.org/article/5cc7a797b47745e3b28950314b19db4f2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/5583031https://doaj.org/toc/1687-5273Error-Correcting Output Codes has become a well-known, established technique for multiclass classification due to its simplicity and efficiency. Each binary split contains different original classes. A noncompetent classifier emerges when it classifies an instance whose real class does not belong to the metasubclasses which is used to learn the classifier. How to reduce the error caused by the noncompetent classifiers under diversity big enough is urgent for ECOC classification. The weighted decoding strategy can be used to reduce the error caused by the noncompetence contradiction through relearning the weight coefficient matrix. To this end, a new weighted decoding strategy taking the classifier competence reliability into consideration is presented in this paper, which is suitable for any coding matrix. Support Vector Data Description is applied to compute the distance from an instance to the metasubclasses. The distance reflects the competence reliability and is fused as the weight in the base classifier combination. In so doing, the effect of the competent classifiers on classification is reinforced, while the bias induced by the noncompetent ones is decreased. Reflecting the competence reliability, the weights of classifiers for each instance change dynamically, which accords with the classification practice. The statistical simulations based on benchmark datasets indicate that our proposed algorithm outperforms other methods and provides new thought for solving the noncompetence problem.Lei LeiYafei SongHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Lei Lei Yafei Song Weighted Decoding for the Competence Reliability Problem of ECOC Multiclass Classification
description	Error-Correcting Output Codes has become a well-known, established technique for multiclass classification due to its simplicity and efficiency. Each binary split contains different original classes. A noncompetent classifier emerges when it classifies an instance whose real class does not belong to the metasubclasses which is used to learn the classifier. How to reduce the error caused by the noncompetent classifiers under diversity big enough is urgent for ECOC classification. The weighted decoding strategy can be used to reduce the error caused by the noncompetence contradiction through relearning the weight coefficient matrix. To this end, a new weighted decoding strategy taking the classifier competence reliability into consideration is presented in this paper, which is suitable for any coding matrix. Support Vector Data Description is applied to compute the distance from an instance to the metasubclasses. The distance reflects the competence reliability and is fused as the weight in the base classifier combination. In so doing, the effect of the competent classifiers on classification is reinforced, while the bias induced by the noncompetent ones is decreased. Reflecting the competence reliability, the weights of classifiers for each instance change dynamically, which accords with the classification practice. The statistical simulations based on benchmark datasets indicate that our proposed algorithm outperforms other methods and provides new thought for solving the noncompetence problem.
format	article
author	Lei Lei Yafei Song
author_facet	Lei Lei Yafei Song
author_sort	Lei Lei
title	Weighted Decoding for the Competence Reliability Problem of ECOC Multiclass Classification
title_short	Weighted Decoding for the Competence Reliability Problem of ECOC Multiclass Classification
title_full	Weighted Decoding for the Competence Reliability Problem of ECOC Multiclass Classification
title_fullStr	Weighted Decoding for the Competence Reliability Problem of ECOC Multiclass Classification
title_full_unstemmed	Weighted Decoding for the Competence Reliability Problem of ECOC Multiclass Classification
title_sort	weighted decoding for the competence reliability problem of ecoc multiclass classification
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/5cc7a797b47745e3b28950314b19db4f
work_keys_str_mv	AT leilei weighteddecodingforthecompetencereliabilityproblemofecocmulticlassclassification AT yafeisong weighteddecodingforthecompetencereliabilityproblemofecocmulticlassclassification
_version_	1718443097857196032

Weighted Decoding for the Competence Reliability Problem of ECOC Multiclass Classification

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