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...
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| フォーマット: | 学位論文 |
| 言語: | English |
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2020
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| オンライン･アクセス: | http://eprints.usm.my/52445/1/Pages%20from%202.%20Final%20Thesis%20Submission.pdf http://eprints.usm.my/52445/ |
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my.usm.eprints.52445 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. |
| institution |
Universiti Sains Malaysia |
| building |
Hamzah Sendut Library |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Sains Malaysia |
| content_source |
USM Institutional Repository |
| url_provider |
http://eprints.usm.my/ |
| language |
English |
| topic |
QA75.5-76.95 Electronic computers. Computer science |
| spellingShingle |
QA75.5-76.95 Electronic computers. Computer science Jothi, Neesha Feature Selection Method Based On Hybrid Filter-Metaheuristic Wrapper Approach |
| 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. |
| format |
Thesis |
| author |
Jothi, Neesha |
| author_facet |
Jothi, Neesha |
| author_sort |
Jothi, Neesha |
| title |
Feature Selection Method Based On Hybrid Filter-Metaheuristic Wrapper Approach |
| title_short |
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_sort |
feature selection method based on hybrid filter-metaheuristic wrapper approach |
| publishDate |
2020 |
| url |
http://eprints.usm.my/52445/1/Pages%20from%202.%20Final%20Thesis%20Submission.pdf http://eprints.usm.my/52445/ |
| _version_ |
1732946310495993856 |
| score |
13.251813 |