AIEOU: Automata-based improved equilibrium optimizer with U-shaped transfer function for feature selection

Abstract

The high dimension of any dataset has become an unavoidable challenge in Data Science and MachineLearning. Reducing the number of dimensions by excluding noisy, irrelevant, or correlated information isoften referred to as the feature selection (FS). The ultimate goal in FS is to identify an optimal set ofdimensions (features) of any dataset to develop an efficient learning model, decrease the computational timeand optimize the memory requirement with the help of some methods. Recently, optimization algorithmshave gained popularity in different fields because of their flexibility and effectiveness. Equilibrium optimizer(EO) is a physics-based meta-heuristic algorithm, which is inspired from a well-mixed dynamic mass balanceon a control volume that has good exploration and exploitation capabilities. In this work, an improvedversion of EO is proposed with the inclusion of learning based automata to find proper values of its parametersand AdaptiveβHill Climbing (AβHC) to find a better equilibrium pool. The method is used as a featureselector, evaluated on 18 standard UCI datasets with the help of K-nearest neighbors (KNN) classifier,and compared with eight state-of-the-art methods including classical and hybrid meta-heuristic algorithms.Moreover, the proposed method is applied on high dimensional Microarray datasets which generally containa few samples but a large number of features, and often lead to a ’curse of dimensionality’. The obtainedresults illustrate the supremacy of the proposed method over the other state-of-the-art methods mentioned inthe literature. The source code of this work is available athttps://github.com/ahmed-shameem/Feature_selection.