Hierarchical Harris hawks optimizer for feature selection

Lemin Peng; Zhennao Cai; Ali Asghar Heidari; Lejun Zhang; Huiling Chen

doi:10.1016/j.jare.2023.01.014

Hierarchical Harris hawks optimizer for feature selection

J Adv Res. 2023 Nov:53:261-278. doi: 10.1016/j.jare.2023.01.014. Epub 2023 Jan 20.

Authors

Lemin Peng¹, Zhennao Cai², Ali Asghar Heidari³, Lejun Zhang⁴, Huiling Chen⁵

Affiliations

¹ Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China. Electronic address: monodies@163.com.
² Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China. Electronic address: cznao@wzu.edu.cn.
³ School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran. Electronic address: as_heidari@ut.ac.ir.
⁴ Cyberspace Institute Advanced Technology, Guangzhou University, Guangzhou 510006, China; College of Information Engineering, Yangzhou University, Yangzhou 225127, China; Research and Development Center for E-Learning , Ministry of Education, Beijing 100039, China. Electronic address: zhanglejun@yzu.edu.cn.
⁵ Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China. Electronic address: chenhuiling.jlu@gmail.com.

Abstract

Introduction: The main feature selection methods include filter, wrapper-based, and embedded methods. Because of its characteristics, the wrapper method must include a swarm intelligence algorithm, and its performance in feature selection is closely related to the algorithm's quality. Therefore, it is essential to choose and design a suitable algorithm to improve the performance of the feature selection method based on the wrapper. Harris hawks optimization (HHO) is a superb optimization approach that has just been introduced. It has a high convergence rate and a powerful global search capability but it has an unsatisfactory optimization effect on high dimensional problems or complex problems. Therefore, we introduced a hierarchy to improve HHO's ability to deal with complex problems and feature selection.

Objectives: To make the algorithm obtain good accuracy with fewer features and run faster in feature selection, we improved HHO and named it EHHO. On 30 UCI datasets, the improved HHO (EHHO) can achieve very high classification accuracy with less running time and fewer features.

Methods: We first conducted extensive experiments on 23 classical benchmark functions and compared EHHO with many state-of-the-art metaheuristic algorithms. Then we transform EHHO into binary EHHO (bEHHO) through the conversion function and verify the algorithm's ability in feature extraction on 30 UCI data sets.

Results: Experiments on 23 benchmark functions show that EHHO has better convergence speed and minimum convergence than other peers. At the same time, compared with HHO, EHHO can significantly improve the weakness of HHO in dealing with complex functions. Moreover, on 30 datasets in the UCI repository, the performance of bEHHO is better than other comparative optimization algorithms.

Conclusion: Compared with the original bHHO, bEHHO can achieve excellent classification accuracy with fewer features and is also better than bHHO in running time.

Keywords: Enhanced hierarchy; Feature selection; HHO; Harris hawks optimizer; Optimization.