Empirical analyses of genetic algorithm and grey wolf optimiser to improve their efficiency with a new multi-objective weighted fitness function for feature selection in machine learning classification: the roadmap

Abstract

ABSTRACT Feature selection (FS) is an optimisation problem that reduces the dimension of the dataset and increases the performance of the machine learning algorithms and classification through the selection of the optimal subset features and elimination of the redundant features. However, the huge search space is an important challenge in the FS problem. Due to their satisfactory capabilities to handle high-dimension search spaces, meta-heuristic search algorithms have recently gained much attention and become popular in the FS problem. For these algorithms, choosing a proper fitness function plays an important role. The fitness function orients the searching strategy of the algorithms to obtain best solutions. Appropriate fitness functions will help the algorithms with exploring the search space more effectively and efficiently. In this work, firstly the efficiency of two of the most outstanding and successful heuristic algorithms in the FS domain, namely genetic algorithm (GA) and grey wolf optimiser (GWO), are investigated and analysed with a single-objective fitness function. Secondly, two recent feature selection techniques based on GA and GWO, namely feature selection, weight, and parameter optimisation (FWP) and binary GWO (BGWO) with their fitness function are investigated and analysed. Thirdly, in order to remove the detected drawbacks and weaknesses of the FS algorithms and to enhance their efficiency, a new multi-objective weighted fitness function based on multiple predominant criteria has been presented. The effectiveness of the proposed fitness function on the FS algorithms is evaluated by using SVM and associative classification on 11 different large and small datasets. The experimental results show the superiority of proposed fitness function (where features were reduced and the classification performance has been improved) over single-objective fitness function and other existing fitness functions. Furthermore, another key aim of this study is to present a comprehensive study about the strengths and weaknesses of the FS algorithms which can be used as guidelines for future possible works to more improve the developments of these algorithms.