A Many-Objective Simultaneous Feature Selection and Discretization for LCS-Based Gesture Recognition

Discretization and feature selection are two relevant techniques for dimensionality reduction. The first one aims to transform a set of continuous attributes into discrete ones, and the second removes the irrelevant and redundant features; these two methods often lead to be more specific and concise...

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Autores principales:	Martin J.-D. Otis, Julien Vandewynckel
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	many-objective optimization evolutionary computation discretization feature selection variable-length problem longest common subsequence Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/e4bee5fc39f1484fbb1138c6f04c9c3e
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spelling	oai:doaj.org-article:e4bee5fc39f1484fbb1138c6f04c9c3e2021-11-11T14:56:52ZA Many-Objective Simultaneous Feature Selection and Discretization for LCS-Based Gesture Recognition10.3390/app112197872076-3417https://doaj.org/article/e4bee5fc39f1484fbb1138c6f04c9c3e2021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/9787https://doaj.org/toc/2076-3417Discretization and feature selection are two relevant techniques for dimensionality reduction. The first one aims to transform a set of continuous attributes into discrete ones, and the second removes the irrelevant and redundant features; these two methods often lead to be more specific and concise data. In this paper, we propose to simultaneously deal with optimal feature subset selection, discretization, and classifier parameter tuning. As an illustration, the proposed problem formulation has been addressed using a constrained many-objective optimization algorithm based on dominance and decomposition (C-MOEA/DD) and a limited-memory implementation of the warping longest common subsequence algorithm (WarpingLCSS). In addition, the discretization sub-problem has been addressed using a variable-length representation, along with a variable-length crossover, to overcome the need of specifying the number of elements defining the discretization scheme in advance. We conduct experiments on a real-world benchmark dataset; compare two discretization criteria as discretization objective, namely Ameva and ur-CAIM; and analyze recognition performance and reduction capabilities. Our results show that our approach outperforms previous reported results by up to 11% and achieves an average feature reduction rate of 80%.Martin J.-D. OtisJulien VandewynckelMDPI AGarticlemany-objective optimizationevolutionary computationdiscretizationfeature selectionvariable-length problemlongest common subsequenceTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 9787, p 9787 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	many-objective optimization evolutionary computation discretization feature selection variable-length problem longest common subsequence Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	many-objective optimization evolutionary computation discretization feature selection variable-length problem longest common subsequence Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Martin J.-D. Otis Julien Vandewynckel A Many-Objective Simultaneous Feature Selection and Discretization for LCS-Based Gesture Recognition
description	Discretization and feature selection are two relevant techniques for dimensionality reduction. The first one aims to transform a set of continuous attributes into discrete ones, and the second removes the irrelevant and redundant features; these two methods often lead to be more specific and concise data. In this paper, we propose to simultaneously deal with optimal feature subset selection, discretization, and classifier parameter tuning. As an illustration, the proposed problem formulation has been addressed using a constrained many-objective optimization algorithm based on dominance and decomposition (C-MOEA/DD) and a limited-memory implementation of the warping longest common subsequence algorithm (WarpingLCSS). In addition, the discretization sub-problem has been addressed using a variable-length representation, along with a variable-length crossover, to overcome the need of specifying the number of elements defining the discretization scheme in advance. We conduct experiments on a real-world benchmark dataset; compare two discretization criteria as discretization objective, namely Ameva and ur-CAIM; and analyze recognition performance and reduction capabilities. Our results show that our approach outperforms previous reported results by up to 11% and achieves an average feature reduction rate of 80%.
format	article
author	Martin J.-D. Otis Julien Vandewynckel
author_facet	Martin J.-D. Otis Julien Vandewynckel
author_sort	Martin J.-D. Otis
title	A Many-Objective Simultaneous Feature Selection and Discretization for LCS-Based Gesture Recognition
title_short	A Many-Objective Simultaneous Feature Selection and Discretization for LCS-Based Gesture Recognition
title_full	A Many-Objective Simultaneous Feature Selection and Discretization for LCS-Based Gesture Recognition
title_fullStr	A Many-Objective Simultaneous Feature Selection and Discretization for LCS-Based Gesture Recognition
title_full_unstemmed	A Many-Objective Simultaneous Feature Selection and Discretization for LCS-Based Gesture Recognition
title_sort	many-objective simultaneous feature selection and discretization for lcs-based gesture recognition
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e4bee5fc39f1484fbb1138c6f04c9c3e
work_keys_str_mv	AT martinjdotis amanyobjectivesimultaneousfeatureselectionanddiscretizationforlcsbasedgesturerecognition AT julienvandewynckel amanyobjectivesimultaneousfeatureselectionanddiscretizationforlcsbasedgesturerecognition AT martinjdotis manyobjectivesimultaneousfeatureselectionanddiscretizationforlcsbasedgesturerecognition AT julienvandewynckel manyobjectivesimultaneousfeatureselectionanddiscretizationforlcsbasedgesturerecognition
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A Many-Objective Simultaneous Feature Selection and Discretization for LCS-Based Gesture Recognition

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