Fuzzy-rough Information Gain Ratio Approach to Filter-wrapper Feature Selection

Feature selection for various applications has been carried out for many years in many different research areas. However, there is a trade-off between finding feature subsets with minimum length and increasing the classification accuracy. In this paper, a filter-wrapper feature selection approach based on fuzzy-rough gain ratio is proposed to tackle this problem. As a search strategy, a modified Ant Colony Optimization (ACO) algorithm is applied on filter phase. ACO has been approved to be a suitable solution in many difficult problems with graph search space such as feature selection. Choosing minimal data reductions among the subsets of features with first and second maximum accuracies is the main contribution of this work. To verify the efficiency of our approach, experiments are performed on 10 well-known UCI data sets. Analysis of the experimental results demonstrates that the proposed approach is able to satisfy two conflicting constraints of feature selection, increasing the classification accuracy as well as decreasing the length of the reduced subsets of features.