Fuzzy-rough discriminative feature selection and classification algorithm, with application to microarray and image datasets

Abstract

Abstract A novel algorithm based on fuzzy-rough sets is proposed for the feature selection and classification of datasets with multiple features, with less computational efforts. The algorithm translates each quantitative value of a feature into fuzzy sets of linguistic terms using membership functions and, identifies the discriminative features. The membership functions are formed by partitioning the feature space into fuzzy equivalence classes, using feature cluster centers identified by the subtractive clustering technique. The lower and upper approximations of the fuzzy equivalence classes are obtained and the discriminative features in the dataset are selected. Classification rules are generated using the fuzzy membership values that partition the lower and upper approximations. The classification is done through a voting process. Both the feature selection and classification algorithms have polynomial time complexity. The algorithm is tested in two types of classification problems namely cancer classification and image pattern classification. The large number of gene expression profiles and relatively small number of available samples make the feature selection a key step in microarray based cancer classification. The proposed algorithm identified the relevant features (predictive genes in the case of cancer data) and provided good classification accuracy, at a less computational cost, with good margin of classification. A comparison of the performance of the proposed classifier with relevant classification methods shows its better discriminative power.