Fast Genetic Algorithm for feature selection — A qualitative approximation approach
Mohammed Ghaith Altarabichi, Sławomir Nowaczyk, Sepideh Pashami, Peyman Sheikholharam Mashhadi
Abstract
We propose a two-stage surrogate-assisted evolutionary approach to address the computational issues arising from using Genetic Algorithm (GA) for feature selection in a wrapper setting for large datasets. The proposed approach involves constructing a lightweight qualitative meta-model by sub-sampling data instances and then using this meta-model to carry out the feature selection task.
Topics & Concepts
Feature selectionComputer scienceSelection (genetic algorithm)Genetic algorithmFeature (linguistics)Task (project management)Artificial intelligenceMachine learningAlgorithmSampling (signal processing)Data miningPattern recognition (psychology)LinguisticsManagementFilter (signal processing)Computer visionEconomicsPhilosophyEvolutionary Algorithms and ApplicationsAdvanced Multi-Objective Optimization AlgorithmsMetaheuristic Optimization Algorithms Research