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.
Highlights
In recent days, the data representation has become one of the essential factors that can significantly affect the performance of classification models
The quadratic transfer function is introduced in quadratic binary Harris hawk optimization (QBHHO) to enhance the performance of binary version of HHO (BHHO) in feature selection
The performances of proposed algorithms are evaluated based on the best fitness value, mean fitness value, standard deviation of fitness value, classification accuracy, and feature size
Summary
The data representation has become one of the essential factors that can significantly affect the performance of classification models. Many researchers adopt metaheuristic algorithms (wrapper methods) to tackle the feature selection problem in classification tasks. We propose the binary version of Harris hawk optimization (HHO) to tackle the feature selection problem in classification tasks. Theorem, no universal metaheuristic algorithm was good at solving all the optimization problems [17] This motivates us to propose a new binary version of HHO in this work. We integrate the S-shaped and V-shaped transfer functions into the algorithm to convert the continuous HHO into the binary version (BHHO) We propose another new variant of HHO, namely quadratic binary Harris hawk optimization (QBHHO) for performance enhancement. The proposed BHHO and QBHHO algorithms are used to solve the feature selection problems as wrapper methods.
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