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An Optimized Hybrid Approach for Feature Selection Based on Chi-Square and Particle Swarm Optimization Algorithms

Author

Listed:
  • Amani Abdo

    (Faculty of Computing, Arab Open University, El-Shorouk 51, Cairo 11211, Egypt
    Faculty of Computers & Artificial Intelligence, Helwan University (HU), Ain Helwan, Cairo 11795, Egypt)

  • Rasha Mostafa

    (Faculty of Computers & Artificial Intelligence, Helwan University (HU), Ain Helwan, Cairo 11795, Egypt)

  • Laila Abdel-Hamid

    (Faculty of Computers & Artificial Intelligence, Helwan University (HU), Ain Helwan, Cairo 11795, Egypt)

Abstract
Feature selection is a significant issue in the machine learning process. Most datasets include features that are not needed for the problem being studied. These irrelevant features reduce both the efficiency and accuracy of the algorithm. It is possible to think about feature selection as an optimization problem. Swarm intelligence algorithms are promising techniques for solving this problem. This research paper presents a hybrid approach for tackling the problem of feature selection. A filter method (chi-square) and two wrapper swarm intelligence algorithms (grey wolf optimization (GWO) and particle swarm optimization (PSO)) are used in two different techniques to improve feature selection accuracy and system execution time. The performance of the two phases of the proposed approach is assessed using two distinct datasets. The results show that PSOGWO yields a maximum accuracy boost of 95.3%, while chi2-PSOGWO yields a maximum accuracy improvement of 95.961% for feature selection. The experimental results show that the proposed approach performs better than the compared approaches.

Suggested Citation

  • Amani Abdo & Rasha Mostafa & Laila Abdel-Hamid, 2024. "An Optimized Hybrid Approach for Feature Selection Based on Chi-Square and Particle Swarm Optimization Algorithms," Data, MDPI, vol. 9(2), pages 1-17, January.
    • Handle: RePEc:gam:jdataj:v:9:y:2024:i:2:p:20-:d:1326020
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