A Hybrid Feature Selection Framework Using Improved Sine Cosine Algorithm with Metaheuristic Techniques

Feature selection is the procedure of extracting the optimal subset of features from an elementary feature set, to reduce the dimensionality of the data. It is an important part of improving the classification accuracy of classification algorithms for big data. Hybrid metaheuristics is one of the most popular methods for dealing with optimization issues. This article proposes a novel feature selection technique called MetaSCA, derived from the standard sine cosine algorithm (SCA). Founded on the SCA, the golden sine section coefficient is added, to diminish the search area for feature selection. In addition, a multi-level adjustment factor strategy is adopted to obtain an equilibrium between exploration and exploitation. The performance of MetaSCA was assessed using the following evaluation indicators: average fitness, worst fitness, optimal fitness, classification accuracy, average proportion of optimal feature subsets, feature selection time, and standard deviation. The performance was measured on the UCI data set and then compared with three algorithms: the sine cosine algorithm (SCA), particle swarm optimization (PSO), and whale optimization algorithm (WOA). It was demonstrated by the simulation data results that the MetaSCA technique had the best accuracy and optimal feature subset in feature selection on the UCI data sets, in most of the cases.

Automated summary

MetaSCA is a novel feature selection technique derived from the standard sine cosine algorithm, which aims to diminish the search area for feature selection by introducing the concept of a golden sine section coefficient and adopting a multi-level adjustment factor strategy for balance between exploration and exploitation. The performance evaluation indicators show that MetaSCA generally outperforms other algorithms, achieving the best accuracy and optimal feature subsets on the UCI data set in most cases.