Map-elites algorithm for features selection problem

Abstract

In the High-dimensional data analysis there are several challenges in the fields of machine learning and data mining. Typically, feature selection is considered as a combinatorial optimization problem which seeks to remove irrelevant and redundant data by reducing computation time and improve learning measures. Given the complexity of this problem, we propose a novel Map-Elites based Algorithm that determines the minimum set of features maximizing learning accuracy simultaneously. Experimental results, on several data based from real scenarios, show the effectiveness of the proposed algorithm.