Next Article in Journal
Detection of Wildfire Smoke Images Based on a Densely Dilated Convolutional Network
Previous Article in Journal
An Energy and Area Efficient Carry Select Adder with Dual Carry Adder Cell
Open AccessArticle

A New Quadratic Binary Harris Hawk Optimization for Feature Selection

1
Fakulti Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia
2
Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia
*
Authors to whom correspondence should be addressed.
Electronics 2019, 8(10), 1130; https://doi.org/10.3390/electronics8101130
Received: 14 September 2019 / Revised: 29 September 2019 / Accepted: 30 September 2019 / Published: 7 October 2019
(This article belongs to the Section Computer Science & Engineering)
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. View Full-Text
Keywords: feature selection; binary optimization; classification; Harris hawk optimization; quadratic transfer function feature selection; binary optimization; classification; Harris hawk optimization; quadratic transfer function
Show Figures

Figure 1

MDPI and ACS Style

Too, J.; Abdullah, A.R.; Mohd Saad, N. A New Quadratic Binary Harris Hawk Optimization for Feature Selection. Electronics 2019, 8, 1130.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop