A Multi-objective hybrid filter-wrapper evolutionary approach for feature selection

Feature selection is an important pre-processing data mining task, which can reduce the data dimensionality and improve not only the classification accuracy but also the classifier efficiency. Filters use statistical characteristics of the data as the evaluation measure rather than using a classification algorithm. On the contrary, the wrapper process is computationally expensive because the evaluation of every feature subset requires running the classifier on the datasets and computing the accuracy from the obtained confusion matrix. In order to solve this problem, we propose a hybrid tri-objective evolutionary algorithm that optimizes two filter objectives, namely the number of features and the mutual information, and one wrapper objective corresponding to the accuracy. Once the population is classified into different non-dominated fronts, only feature subsets belonging to the first (best) one are improved using the indicator-based multi-objective local search. Our proposed hybrid algorithm, named Filter-Wrapper-based Nondominated Sorting Genetic Algorithm-II, is compared against several multi-objective and single-objective feature selection algorithms on eighteen benchmark datasets having different dimensionalities. Experimental results show that our proposed algorithm gives competitive and better results with respect to existing algorithms.