Title: Intelligent system for feature selection based on rough set and chaotic binary grey wolf optimisation

Authors: Ahmad Taher Azar; Ahmed M. Anter; Khaled M. Fouad

Addresses: Robotics and Internet-of-Things Lab (RIOTU), Prince Sultan University, Riyadh, Saudi Arabia; Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt ' Faculty of Computers and Information, Beni-Suef University, Beni Suef, Egypt ' Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt

Abstract: Feature Selection (FS) has a non-trivial role in supervised learning; like classification, for many causes. FS aims at facilitating the model processes and reducing the computation time. In feature selection, trivial features are eliminated from the data to produce transparently and comprehensibly a model. Furthermore, a feature selection process can decrease noise data; wherefore, feature selection enhances the accuracy measure of the classification process. This paper proposes a robust hybrid dynamic model for feature selection, called RS-CBGWO-FS. RS-CBGWO-FS is a combination of Rough Set (RS), chaos theory and Binary Grey Wolf Optimisation (BGWO). GWO parameters are estimated and tuned by using ten various chaotic maps. Five complex medical data sets are used in the evaluation experiments. The selected data sets have various uncertainty attributes and missing values. The overall result indicates that RS-CBGWO-FS with the Singer and piecewise chaos maps provides better effectiveness, minimal error, higher convergence speed and lower computation time.

Keywords: GWO; grey wolf optimisation; meta-heuristics; rough set theory; chaos theory; feature reduction and selection; data classification.

DOI: 10.1504/IJCAT.2020.107901

International Journal of Computer Applications in Technology, 2020 Vol.63 No.1/2, pp.4 - 24

Received: 27 Jun 2019
Accepted: 04 Oct 2019
Published online: 30 Jun 2020 *

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