A Quasi-Oppositional Based Flamingo Search Algorithm Integrated with Generalized Ring Crossover for Effective Feature Selection

Abstract

ABSTRACT The goal of this article is to present a new hybrid technique for solving the feature selection problem. Conventionally, the process of determining the most relevant subset based on the given criteria is known as feature selection. Specifically, feature selection is a real world issue that can be tackled by an optimization technique. In the proposed method, a novel hybrid Quasi oppositional based Flamingo search algorithm with Generalized Ring Crossover (QOFSA-GRC) model is introduced to pick the most relevant features from the dataset. Specifically, the Quasi oppositional based Flamingo search algorithm (QOFSA) generates two populations one by Quasi oppositional based learning and the other by the Flamingo search algorithm to resolve the issue known as the curse of dimensionality. Then, by utilizing generalized ring crossover, the multiple relevant features are selected from the UCI repository dataset. Finally, the Kernel Extreme Learning Machine (KELM) classifier validates the selected features. The performance of the proposed model is tested by 20 UCI benchmark datasets and the outcomes are compared with other models. Through experimental outcomes, it has been revealed that the suggested model produces the best accuracy and also selects fewer numbers of features that are more relevant from the dataset. In terms of other performance measures, the proposed model attains better outcomes for the majority of the datasets.