Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification

IEEE Access

Seyedali Mirjalili¹  Mohammed G. Ragab²  Helmi Md Rais²  Qasem Al-Tashi²  Said Jadid Abdulkadir²  Hitham Alhussian²  Alawi Alqushaibi² 

[1] Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, QLD, Australia;Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia;

关键词: Feature selection;    grey wolf optimizer;    multi-objective optimization;    classification;   

DOI  :  10.1109/ACCESS.2020.3000040

来源: DOAJ

【 摘 要 】

Feature selection or dimensionality reduction can be considered as a multi-objective minimization problem with two objectives: minimizing the number of features and minimizing the error rate simultaneously. Despite being a multi-objective problem, most existing approaches treat feature selection as a single-objective optimization problem. Recently, Multi-objective Grey Wolf optimizer (MOGWO) was proposed to solve multi-objective optimization problem. However, MOGWO was originally designed for continuous optimization problems and hence, it cannot be utilized directly to solve multi-objective feature selection problems which are inherently discrete in nature. Therefore, in this research, a binary version of MOGWO based on sigmoid transfer function called BMOGW-S is developed to optimize feature selection problems. A wrapper based Artificial Neural Network (ANN) is used to assess the classification performance of a subset of selected features. To validate the performance of the proposed method, 15 standard benchmark datasets from the UCI repository are employed. The proposed BMOGWO-S was compared with MOGWO with a tanh transfer function and Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Particle Swarm Optimization (MOPSO). The results showed that the proposed BMOGWO-S can effectively determine a set of non-dominated solutions. The proposed method outperforms the existing multi-objective approaches in most cases in terms of features reduction as well as classification error rate while benefiting from a lower computational cost.

【 授权许可】

Unknown