Feature selection method based on chaotic maps and butterfly optimization algorithm

Awad, AA; Ali, AF; Gaber, T

Abstract

Feature selection (FS) is a challenging problem that attracted
the attention of many researchers. FS can be considered as an NP hard
problem, If dataset contains N features then 2N solutions are generated
with each additional feature, the complexity doubles. To solve this problem, we reduce the dimensionality of the feature by extracting the most
important features. In this paper we integrate the chaotic maps in the
standard butterfly optimization algorithm to increase the diversity and
avoid trapping in local minima in this algorithm.. The proposed algorithm is called Chaotic Butterfly Optimization Algorithm (CBOA).The
performance of the proposed CBOA is investigated by applying it on 16
benchmark datasets and comparing it against six meta-heuristics algorithms. The results show that invoking the chaotic maps in the standard
BOA can improve its performance with accuracy more than 95%.

Citation

Awad, A., Ali, A., & Gaber, T. Feature selection method based on chaotic maps and butterfly optimization algorithm. Presented at International Conference on Artificial Intelligence and Computer Vision (AICV 2020), Cairo, Egypt

• Presentation Conference Type: Other
• Conference Name: International Conference on Artificial Intelligence and Computer Vision (AICV 2020)
• Conference Location: Cairo, Egypt
• End Date: Apr 10, 2020
• Acceptance Date: Dec 15, 2019
• Online Publication Date: Mar 24, 2020
• Publication Date: Mar 24, 2020
• Deposit Date: Mar 3, 2020
• Publicly Available Date: Mar 24, 2021
• Series Title: Advances in Intelligent Systems and Computing
• Series Number: 1153
• Book Title: Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020)
• ISBN: 9783030442880-(print);-9783030442897-(online)
• DOI: https://doi.org/10.1007/978-3-030-44289-7_16
• Publisher URL: https://doi.org/10.1007/978-3-030-44289-7_16
• Related Public URLs: http://egyptscience.net/AICV2020/home.html; https://doi.org/10.1007/978-3-030-44289-7
• Additional Information: Event Type : Conference