INTUITIONISTIC FUZZY REASONING FOR MULTIPLE TWO-CLASS CLASSIFIERS FUSION

Abstract

Combining outputs of a pool of individual classifiers appropriately, as a hot research topic of pattern classification, can generate statistically significant increase in classification performances. During the last decades, several fusion algorithms were presented, but few of those focus on two-class classification which possesses wide application area such as sentiment classification, cancer differentiation and so on. Thus the main purpose of this paper is to develop a highly effective fusion algorithm, i.e. intuitionistic fuzzy reasoning fusion algorithm, to increase the performance of a multiple two-class classifiers system. The outputs of component classifiers are represented by a set of intuitionistic fuzzy values at first and the fusion process is interpreted as aggregation of intuitionistic fuzzy information. The proposed algorithm includes three versions using the intuitionistic fuzzy arithmetic average operator, intuitionistic fuzzy weighted average operator and induced intuitionistic fuzzy ordered weighted average aggregation operator, respectively. The proposed fusion algorithm can combine both evidences of the hypothesis that a test pattern belongs to a class and evidences of the hypothesis that the test pattern does not belong to the class at the same time. We compare the versions of our algorithm with four other fusion techniques on five open-access datasets. The proposed algorithm exhibits good predictive abilities, compared to the best individual classifiers and other comparable fusion techniques. Further, the experiments show some interesting results about measuring and weighting component classifiers.