Multi-Objective Evolutionary Algorithms and Metaheuristics for Feature Selection: a Review

Abstract

In the areas of machine learning / big data, when collecting data, sometimes too many features may be stored. Some of them may be redundant or irrelevant for the problem to be solved, adding noise to the dataset. Feature selection allows to create a subset from the original feature set, according to certain criteria. By creating a smaller subset of relevant features, it is possible to improve the learning accuracy while reducing the amount of data. This means means better results obtained in a shorter learning time. However, feature selection is normally regarded as a very important problem to be solved, as it directly impacts both data analysis and model creation. The problem of optimizing the selected features of a given dataset is not always trivial but, throughout the years, different ways to counter this optimization problem have been presented. This work presents how feature selection fits in the larger context of multi-objective problems as well as a review of how both multi-objective evolutionary algorithms and metaheuristics are being used in order to solve feature selection problems.