A New Quadratic Binary Harris Hawk Optimization For Feature Selection

Abdullah, Abdul Rahim and Too, Jing Wei and Mohd Saad, Norhashimah (2019) A New Quadratic Binary Harris Hawk Optimization For Feature Selection. Electronics (Switzerland), 8 (10). pp. 1-27. ISSN 2079-9292

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Abstract

Harris hawk optimization (HHO) is one of the recently proposed metaheuristic algorithms that has proven to be work more effectively in several challenging optimization tasks. However, the original HHO is developed to solve the continuous optimization problems, but not to the problems with binary variables. This paper proposes the binary version of HHO (BHHO) to solve the feature selection problem in classification tasks. The proposed BHHO is equipped with an S-shaped or V-shaped transfer function to convert the continuous variable into a binary one. Moreover, another variant of HHO, namely quadratic binary Harris hawk optimization (QBHHO), is proposed to enhance the performance of BHHO. In this study, twenty-two datasets collected from the UCI machine learning repository are used to validate the performance of proposed algorithms. A comparative study is conducted to compare the effectiveness of QBHHO with other feature selection algorithms such as binary differential evolution (BDE), genetic algorithm (GA), binary multi-verse optimizer (BMVO), binary flower pollination algorithm (BFPA), and binary salp swarm algorithm (BSSA). The experimental results show the superiority of the proposed QBHHO in terms of classification performance, feature size, and fitness values compared to other algorithms.

Item Type:	Article
Uncontrolled Keywords:	Binary optimization, Classification, Feature selection, Harris hawk optimization, Quadratic transfer function
Divisions:	Faculty of Electrical Engineering
Depositing User:	Sabariah Ismail
Date Deposited:	29 Jul 2020 12:59
Last Modified:	29 Jul 2020 12:59
URI:	http://eprints.utem.edu.my/id/eprint/24171
Statistic Details:	View Download Statistic

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