Evolutionary Multitask Optimization for Multiform Feature Selection in Classification.

Feature selection (FS) is a significant research topic in machine learning and artificial intelligence, but it becomes complicated in the high dimensional search space due to the vast number of features. Evolutionary computation (EC) has been widely used in solving FS by modeling it as an expensive wrapper-form optimization task, where a classifier is used to obtain classification accuracy for fitness evaluation (FE). In this article, we propose that the FS problem can be also modeled as a cheap filter-form optimization task, where the FE is based on the relevance and redundancy of the selected features.