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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/21/9787 |
| _version_ | 1797512958373265408 |
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| author | Martin J.-D. Otis Julien Vandewynckel |
| author_facet | Martin J.-D. Otis Julien Vandewynckel |
| author_sort | Martin J.-D. Otis |
| collection | DOAJ |
| 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%. |
| first_indexed | 2024-03-10T06:08:02Z |
| format | Article |
| id | doaj.art-e4bee5fc39f1484fbb1138c6f04c9c3e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T06:08:02Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e4bee5fc39f1484fbb1138c6f04c9c3e2023-11-22T20:23:07ZengMDPI AGApplied Sciences2076-34172021-10-011121978710.3390/app11219787A Many-Objective Simultaneous Feature Selection and Discretization for LCS-Based Gesture RecognitionMartin J.-D. Otis0Julien Vandewynckel1LAR.i Lab, University of Quebec at Chicoutimi, Saguenay, QC G7H 2B1, CanadaLAR.i Lab, University of Quebec at Chicoutimi, Saguenay, QC G7H 2B1, CanadaDiscretization 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%.https://www.mdpi.com/2076-3417/11/21/9787many-objective optimizationevolutionary computationdiscretizationfeature selectionvariable-length problemlongest common subsequence |
| spellingShingle | Martin J.-D. Otis Julien Vandewynckel A Many-Objective Simultaneous Feature Selection and Discretization for LCS-Based Gesture Recognition Applied Sciences many-objective optimization evolutionary computation discretization feature selection variable-length problem longest common subsequence |
| title | 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_short | 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 |
| topic | many-objective optimization evolutionary computation discretization feature selection variable-length problem longest common subsequence |
| url | https://www.mdpi.com/2076-3417/11/21/9787 |
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