Framework of Meta-Heuristic Variable Length Searching for Feature Selection in High-Dimensional Data
Feature Selection in High Dimensional Space is a combinatory optimization problem with an NP-hard nature. Meta-heuristic searching with embedding information theory-based criteria in the fitness function for selecting the relevant features is used widely in current feature selection algorithms. Howe...
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| Format: | Article |
| Language: | English |
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Mdpi
2023
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| Online Access: | http://eprints.uthm.edu.my/9652/ http://eprints.uthm.edu.my/9652/1/J16188_001cf5dff0bc3a42365b2bcdb33bad98.pdf |
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| author | Qadir Sara, Tara Othman Fuad, Norfaiza Md Taujuddin, Nik Shahidah Afifi |
| author_facet | Qadir Sara, Tara Othman Fuad, Norfaiza Md Taujuddin, Nik Shahidah Afifi |
| author_sort | Qadir Sara, Tara Othman |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | Feature Selection in High Dimensional Space is a combinatory optimization problem with an NP-hard nature. Meta-heuristic searching with embedding information theory-based criteria in the fitness function for selecting the relevant features is used widely in current feature selection algorithms. However, the increase in the dimension of the solution space leads to a high computational cost and
risk of convergence. In addition, sub-optimality might occur due to the assumption of a certain length of the optimal number of features. Alternatively, variable length searching enables searching within the variable length of the solution space, which leads to more optimality and less computational load. The literature contains various meta-heuristic algorithms with variable length searching. All of them
enable searching in high dimensional problems. However, an uncertainty in their performance exists. In order to fill this gap, this article proposes a novel framework for comparing various variants of variable length-searching meta-heuristic algorithms in the application of feature selection. For this
purpose, we implemented four types of variable length meta-heuristic searching algorithms, namely VLBHO-Fitness, VLBHO-Position, variable length particle swarm optimization (VLPSO) and genetic variable length (GAVL), and we compared them in terms of classification metrics. The evaluation showed the overall superiority of VLBHO over the other algorithms in terms of accomplishing lower
fitness values when optimizing mathematical functions of the variable length type. |
| first_indexed | 2025-11-15T20:30:55Z |
| format | Article |
| id | uthm-9652 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:30:55Z |
| publishDate | 2023 |
| publisher | Mdpi |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-96522023-08-16T07:11:01Z http://eprints.uthm.edu.my/9652/ Framework of Meta-Heuristic Variable Length Searching for Feature Selection in High-Dimensional Data Qadir Sara, Tara Othman Fuad, Norfaiza Md Taujuddin, Nik Shahidah Afifi T Technology (General) Feature Selection in High Dimensional Space is a combinatory optimization problem with an NP-hard nature. Meta-heuristic searching with embedding information theory-based criteria in the fitness function for selecting the relevant features is used widely in current feature selection algorithms. However, the increase in the dimension of the solution space leads to a high computational cost and risk of convergence. In addition, sub-optimality might occur due to the assumption of a certain length of the optimal number of features. Alternatively, variable length searching enables searching within the variable length of the solution space, which leads to more optimality and less computational load. The literature contains various meta-heuristic algorithms with variable length searching. All of them enable searching in high dimensional problems. However, an uncertainty in their performance exists. In order to fill this gap, this article proposes a novel framework for comparing various variants of variable length-searching meta-heuristic algorithms in the application of feature selection. For this purpose, we implemented four types of variable length meta-heuristic searching algorithms, namely VLBHO-Fitness, VLBHO-Position, variable length particle swarm optimization (VLPSO) and genetic variable length (GAVL), and we compared them in terms of classification metrics. The evaluation showed the overall superiority of VLBHO over the other algorithms in terms of accomplishing lower fitness values when optimizing mathematical functions of the variable length type. Mdpi 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/9652/1/J16188_001cf5dff0bc3a42365b2bcdb33bad98.pdf Qadir Sara, Tara Othman and Fuad, Norfaiza and Md Taujuddin, Nik Shahidah Afifi (2023) Framework of Meta-Heuristic Variable Length Searching for Feature Selection in High-Dimensional Data. Computers 2, 12 (7). pp. 1-13. https://doi.org/10.3390/computers12010007 |
| spellingShingle | T Technology (General) Qadir Sara, Tara Othman Fuad, Norfaiza Md Taujuddin, Nik Shahidah Afifi Framework of Meta-Heuristic Variable Length Searching for Feature Selection in High-Dimensional Data |
| title | Framework of Meta-Heuristic Variable Length Searching for
Feature Selection in High-Dimensional Data |
| title_full | Framework of Meta-Heuristic Variable Length Searching for
Feature Selection in High-Dimensional Data |
| title_fullStr | Framework of Meta-Heuristic Variable Length Searching for
Feature Selection in High-Dimensional Data |
| title_full_unstemmed | Framework of Meta-Heuristic Variable Length Searching for
Feature Selection in High-Dimensional Data |
| title_short | Framework of Meta-Heuristic Variable Length Searching for
Feature Selection in High-Dimensional Data |
| title_sort | framework of meta-heuristic variable length searching for
feature selection in high-dimensional data |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/9652/ http://eprints.uthm.edu.my/9652/ http://eprints.uthm.edu.my/9652/1/J16188_001cf5dff0bc3a42365b2bcdb33bad98.pdf |