- author = "Mohammad Sadegh Khorshidi and Danial Yazdani and Jacek Mandziuk and Mohammad Reza Nikoo and Amir H. Gandomi",
- title = "A Filter-Based Feature Selection and Ranking Approach to Enhance Genetic Programming for High-Dimensional Data Analysis",
- booktitle = "2023 IEEE Congress on Evolutionary Computation (CEC)",
- year = "2023",
- editor = "Gui DeSouza and Gary Yen",
- address = "Chicago, USA",
- month = "1-5 " # jul,
- keywords = "genetic algorithms, genetic programming, Multi-Stage Genetic Programming, Information Theory, Feature Selection, Feature Ranking, High-DimensionalData, Data Analytics",
- isbn13 = "979-8-3503-1459-5",
-
URL = "
https://squ.elsevierpure.com/en/publications/a-filter-based-feature-selection-and-ranking-approach-to-enhance-",
-
DOI = "
doi:10.1109/CEC53210.2023.10254048",
- size = "9 pages",
- abstract = "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.",
-
notes = "
CEC2023 https://2023.ieee-cec.org/program-html/",
