A Novel Hybrid Algorithm for Feature Selection Based on Whale Optimization Algorithm

Abstract

Feature selection enhances classification accuracy by removing irrelevant and redundant feature. Feature selection plays an important role in data mining and pattern recognition. In this paper, we propose a hybrid feature subset selection algorithm called the maximum Pearson maximum distance improved whale optimization algorithm (MPMDIWOA). First, based on Pearson's correlation coefficient and correlation distance, a filter algorithm is proposed named maximum Pearson maximum distance (MPMD). Two parameters are proposed in MPMD to adjust the weights of the relevance and redundancy. Second, the modified whale optimization algorithm can act as a wrapper algorithm. After introducing the maximum value without change (MVWC) and the threshold, the filter algorithm and the wrapper algorithm are combined to form an algorithm called MPMDIWOA. In MPMDIWOA, the filter algorithm and wrapper algorithm are called different times according to changes in MVWC and threshold. Finally, the optimal classification accuracy was found. The proposed method is tested on 10 benchmark datasets from UCI machine learning databases. The experimental results show that the classification accuracy of the proposed algorithm is significantly higher than that of the other three wrapper algorithms and one hybrid algorithm.

A Novel Hybrid Feature Selection and Ensemble Learning Framework for Unbalanced Cancer Data Diagnosis With Transcriptome and Functional Proteomic

“…The selected miRNA used SVM to classify the breast cancer subtypes. Literature [24] proposed Pearson's correlation coefficient and correlation distance as the filter algorithm, and the modified whale optimization algorithm as a wrapper algorithm to classify the UCI datasets. Literature [25] adopted reliefF, chi-square, fisher score and binary grasshopper optimization algorithm as the hybrid method in classification of the cancer datasets.…”

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

“…And the new version was applied to fourteen datasets. There are some other versions of WOA [205]- [207] which are presented in solving feature selection problems.…”

Dynamic Butterfly Optimization Algorithm for Feature Selection

“…In the literature, there are several studies that applied optimisation algorithms for feature selection problem. For example, [3,4] improved whale optimisation algorithm (WOA) and used it for feature selection, [5] improved particle swarm optimisation (PSO) and used it for feature selection, [6] used Grasshopper optimisation algorithm (GOA) for feature selection, [7] used Firefly algorithm (FFA) for feature selection, [8] used Differential Evolution (DE) for feature selection, [1] improved Salp Swarm Algorithm (SSA)and used it for feature selection, [9] used Genetic Algorithm (GA) for feature selection, [10] used Grey Wolf Optimization (GWO) for feature selection, [11] improved Gravitational Search algorithm (GSA) and used it for feature selection, [12] used fish swarm optimisation (FSO) for feature selection, [13] improved crow search algorithm (CSA) and used it for feature selection, [14] improved Dragonfly Algorithm (DA) and used it for feature selection, [15] improved social spider algorithm (SSA) and used it for feature selection, [1,16] improved Salp Swarm Algorithm (SSA) and used it for feature selection, [2] improved Harris hawks optimisation (HHO) and used it for feature selection.…”