A fuzzy-rough approach for finding various minimal data reductions using ant colony optimization

摘要

Finding out information about features is one of the main goals of feature selection. In that case there is not particularly care about the resulting classification accuracy, but we are interested in maximizing dependency degree. A data set can have several minimal data reductions; however, most of the methods are able to find only one minimal data reduction which is not beneficial. In this paper, we propose a feature selection method based on modified Ant Colony Optimization algorithm (ACO). The main contribution of this paper includes using fuzzy-rough gain ratio as heuristic information and new rules for pheromone updating in ACO. Unlike most of the methods which find only one minimal reduction, this method is able to find various minimal data reductions. The proposed method is compared with three other meta-heuristic methods. The results show that our approach is very successful in finding various minimal data reductions.