Fuzzy kernel feature selection with multi-objective differential evolution algorithm
In this paper, we propose a multi-objective differential evolution-based filter approach for feature selection that interconnects fuzzy- and kernel-based information theory measures to find feature subsets that are optimal responses to the targets. In contrast to the existing filter approaches using...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2019-10-01
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| Series: | Connection Science |
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| Online Access: | http://dx.doi.org/10.1080/09540091.2019.1639624 |
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| _version_ | 1850176603780284416 |
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| author | Emrah Hancer |
| author_facet | Emrah Hancer |
| author_sort | Emrah Hancer |
| collection | DOAJ |
| description | In this paper, we propose a multi-objective differential evolution-based filter approach for feature selection that interconnects fuzzy- and kernel-based information theory measures to find feature subsets that are optimal responses to the targets. In contrast to the existing filter approaches using the principles of information theory and rough set theory, our approach can be applied to continuous datasets without discretisation. Moreover, our study is the first in the literature that employs fuzzy and kernel measures to form a filter criterion for feature selection, to our knowledge. We prove various favourable results using a variety of benchmark datasets and also demonstrate that our approach can better search the dimensionality space to reach maximum predictive of the response. |
| format | Article |
| id | doaj-art-c19447a085fc498ba1da77db5a134761 |
| institution | OA Journals |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2019-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-c19447a085fc498ba1da77db5a1347612025-08-20T02:19:12ZengTaylor & Francis GroupConnection Science0954-00911360-04942019-10-0131432334110.1080/09540091.2019.16396241639624Fuzzy kernel feature selection with multi-objective differential evolution algorithmEmrah Hancer0Mehmet Akif Ersoy UniversityIn this paper, we propose a multi-objective differential evolution-based filter approach for feature selection that interconnects fuzzy- and kernel-based information theory measures to find feature subsets that are optimal responses to the targets. In contrast to the existing filter approaches using the principles of information theory and rough set theory, our approach can be applied to continuous datasets without discretisation. Moreover, our study is the first in the literature that employs fuzzy and kernel measures to form a filter criterion for feature selection, to our knowledge. We prove various favourable results using a variety of benchmark datasets and also demonstrate that our approach can better search the dimensionality space to reach maximum predictive of the response.http://dx.doi.org/10.1080/09540091.2019.1639624kernel spacefuzzy information theorymulti-objective optimisationdifferential evolutionfeature selection |
| spellingShingle | Emrah Hancer Fuzzy kernel feature selection with multi-objective differential evolution algorithm Connection Science kernel space fuzzy information theory multi-objective optimisation differential evolution feature selection |
| title | Fuzzy kernel feature selection with multi-objective differential evolution algorithm |
| title_full | Fuzzy kernel feature selection with multi-objective differential evolution algorithm |
| title_fullStr | Fuzzy kernel feature selection with multi-objective differential evolution algorithm |
| title_full_unstemmed | Fuzzy kernel feature selection with multi-objective differential evolution algorithm |
| title_short | Fuzzy kernel feature selection with multi-objective differential evolution algorithm |
| title_sort | fuzzy kernel feature selection with multi objective differential evolution algorithm |
| topic | kernel space fuzzy information theory multi-objective optimisation differential evolution feature selection |
| url | http://dx.doi.org/10.1080/09540091.2019.1639624 |
| work_keys_str_mv | AT emrahhancer fuzzykernelfeatureselectionwithmultiobjectivedifferentialevolutionalgorithm |