A New Feature Selection Criterion for Fuzzy Classification

The identification of fuzzy models for classification is a very complex task. Often, real world databases have a large number of features and the most relevant ones must be chosen. Therefore, it is necessary to select carefully the variables that are relevant for the feature class. A new automatic feature selection for classification problems is proposed in this paper, to construct compact fuzzy classification models. Several clustering algorithms are used and compared in terms of computational efficiency and accuracy in classification problems. The proposed algorithm was tested in well-known data sets: iris plant, wine, hepatitis, breast cancer and in a difficult real-world problem: the prediction of bankruptcy. The experiments show the advantages of the proposed method for selecting the proper features for classification.