Feature Selection Method Based On Hybrid Filter-Metaheuristic Wrapper Approach
High dimension data are often associated with redundant features and there exist many information-theoretic approaches used to select the most relevant set of features and to reduce the feature size. The three most significant approaches are filter, wrap- per, and embedded approaches. Most filter ap...
| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2020
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| Online Access: | http://eprints.usm.my/52445/ http://eprints.usm.my/52445/1/Pages%20from%202.%20Final%20Thesis%20Submission.pdf |
| _version_ | 1848882258712199168 |
|---|---|
| author | Jothi, Neesha |
| author_facet | Jothi, Neesha |
| author_sort | Jothi, Neesha |
| building | USM Institutional Repository |
| collection | Online Access |
| description | High dimension data are often associated with redundant features and there exist many information-theoretic approaches used to select the most relevant set of features and to reduce the feature size. The three most significant approaches are filter, wrap- per, and embedded approaches. Most filter approaches fail to identify the individual contribution of every feature in each set of features in achieving an optimal feature subset. While the wrapper approaches encounter problems from complex interactions among features and stagnation in local optima. To address, these drawbacks, this study investigates filter and wrapper approaches to develop effective hybrid approaches for feature selection. |
| first_indexed | 2025-11-15T18:32:04Z |
| format | Thesis |
| id | usm-52445 |
| institution | Universiti Sains Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T18:32:04Z |
| publishDate | 2020 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | usm-524452022-04-28T08:39:09Z http://eprints.usm.my/52445/ Feature Selection Method Based On Hybrid Filter-Metaheuristic Wrapper Approach Jothi, Neesha QA75.5-76.95 Electronic computers. Computer science High dimension data are often associated with redundant features and there exist many information-theoretic approaches used to select the most relevant set of features and to reduce the feature size. The three most significant approaches are filter, wrap- per, and embedded approaches. Most filter approaches fail to identify the individual contribution of every feature in each set of features in achieving an optimal feature subset. While the wrapper approaches encounter problems from complex interactions among features and stagnation in local optima. To address, these drawbacks, this study investigates filter and wrapper approaches to develop effective hybrid approaches for feature selection. 2020-11 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/52445/1/Pages%20from%202.%20Final%20Thesis%20Submission.pdf Jothi, Neesha (2020) Feature Selection Method Based On Hybrid Filter-Metaheuristic Wrapper Approach. PhD thesis, Universiti Sains Malaysia. |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science Jothi, Neesha Feature Selection Method Based On Hybrid Filter-Metaheuristic Wrapper Approach |
| title | Feature Selection Method Based On Hybrid Filter-Metaheuristic Wrapper Approach |
| title_full | Feature Selection Method Based On Hybrid Filter-Metaheuristic Wrapper Approach |
| title_fullStr | Feature Selection Method Based On Hybrid Filter-Metaheuristic Wrapper Approach |
| title_full_unstemmed | Feature Selection Method Based On Hybrid Filter-Metaheuristic Wrapper Approach |
| title_short | Feature Selection Method Based On Hybrid Filter-Metaheuristic Wrapper Approach |
| title_sort | feature selection method based on hybrid filter-metaheuristic wrapper approach |
| topic | QA75.5-76.95 Electronic computers. Computer science |
| url | http://eprints.usm.my/52445/ http://eprints.usm.my/52445/1/Pages%20from%202.%20Final%20Thesis%20Submission.pdf |