A Filter-Based Feature Selection and Ranking Approach to Enhance Genetic Programming for High-Dimensional Data Analysis
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 IEEE Congress on Evolutionary Computation (CEC), 2023, 00, pp. 1-9
- Issue Date:
- 2023-09-25
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| GP - IEEE CEC 2023.pdf | Accepted version | 949.66 kB |
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Genetic programming GP as a predictive data analytic tool has difficulties dealing with high dimensional problems Therefore some GP variants have been proposed for this type of problem such as multi stage GP MSGP Filter based feature selection is commonly used in the literature for various machine learning purposes However its application for GP is overlooked due to GP s capability to operate as a wrapper based feature selection while trying to find an optimal expression of the target variable via a functional combination of predictors The effectiveness of wrapper and filer based feature selection approaches in machine learning has been the subject of a long standing debate in the literature This study aims to introduce an efficient feature selection approach and couple it with MSGP in order to handle high dimensional problems In addition the stages of the GP are systematically ordered based on the variables information The proposed approach is tested against five real high dimensional datasets The results show that GP s inherent wrapper feature selection ability can be advanced further by using a filter based feature selection approach to shrink the search space which results in improving computational costs expression complexity and the accuracy of MSGP
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