Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems
Feature selection is an important task to improve the classifier’s accuracy and to decrease the problem size. A number of methodologies have been presented for feature selection problems using metaheuristic algorithms. In this paper, an improved self-adaptive inertia weight particle swarm optimisati...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2020-01-01
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| Series: | Connection Science |
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| Online Access: | http://dx.doi.org/10.1080/09540091.2019.1609419 |
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| _version_ | 1850254970770685952 |
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| author | Arfan Ali Nagra Fei Han Qing Hua Ling Muhammad Abubaker Farooq Ahmad Sumet Mehta Abeo Timothy Apasiba |
| author_facet | Arfan Ali Nagra Fei Han Qing Hua Ling Muhammad Abubaker Farooq Ahmad Sumet Mehta Abeo Timothy Apasiba |
| author_sort | Arfan Ali Nagra |
| collection | DOAJ |
| description | Feature selection is an important task to improve the classifier’s accuracy and to decrease the problem size. A number of methodologies have been presented for feature selection problems using metaheuristic algorithms. In this paper, an improved self-adaptive inertia weight particle swarm optimisation with local search and combined with C4.5 classifiers for feature selection algorithm is proposed. In this proposed algorithm, the gradient base local search with its capacity of helping to explore the feature space and an improved self-adaptive inertia weight particle swarm optimisation with its ability to converge a best global solution in the search space. Experimental results have verified that the SIW-APSO-LS performed well compared with other state of art feature selection techniques on a suit of 16 standard data sets. |
| format | Article |
| id | doaj-art-5eb03ea9285845e298b1fc6dfdb7fb11 |
| institution | OA Journals |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-5eb03ea9285845e298b1fc6dfdb7fb112025-08-20T01:57:00ZengTaylor & Francis GroupConnection Science0954-00911360-04942020-01-01321163610.1080/09540091.2019.16094191609419Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problemsArfan Ali Nagra0Fei Han1Qing Hua Ling2Muhammad Abubaker3Farooq Ahmad4Sumet Mehta5Abeo Timothy Apasiba6Jiangsu UniversityJiangsu UniversityJiangsu UniversitySchool of Electrical and Information Engineering, Jiangsu UniversityCOMSATS University, Lahore CampusJiangsu UniversityJiangsu UniversityFeature selection is an important task to improve the classifier’s accuracy and to decrease the problem size. A number of methodologies have been presented for feature selection problems using metaheuristic algorithms. In this paper, an improved self-adaptive inertia weight particle swarm optimisation with local search and combined with C4.5 classifiers for feature selection algorithm is proposed. In this proposed algorithm, the gradient base local search with its capacity of helping to explore the feature space and an improved self-adaptive inertia weight particle swarm optimisation with its ability to converge a best global solution in the search space. Experimental results have verified that the SIW-APSO-LS performed well compared with other state of art feature selection techniques on a suit of 16 standard data sets.http://dx.doi.org/10.1080/09540091.2019.1609419classificationfeature selectionadaptive particle swarm optimisationgradient base local searchinertia weight |
| spellingShingle | Arfan Ali Nagra Fei Han Qing Hua Ling Muhammad Abubaker Farooq Ahmad Sumet Mehta Abeo Timothy Apasiba Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems Connection Science classification feature selection adaptive particle swarm optimisation gradient base local search inertia weight |
| title | Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems |
| title_full | Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems |
| title_fullStr | Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems |
| title_full_unstemmed | Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems |
| title_short | Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems |
| title_sort | hybrid self inertia weight adaptive particle swarm optimisation with local search using c4 5 decision tree classifier for feature selection problems |
| topic | classification feature selection adaptive particle swarm optimisation gradient base local search inertia weight |
| url | http://dx.doi.org/10.1080/09540091.2019.1609419 |
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