Fuzzy If-Then Rules Classifier on Ensemble Data

Tien Thanh Nguyen, Alan Wee-Chung Liew, Cuong To, Xuan Cuong Pham, Mai Phuong Nguyen

Abstract: This paper introduces a novel framework that uses fuzzy IF-THEN rules in an ensemble system. Our model tackles several drawbacks. First, IF-THEN rules approaches have problems with high dimensional data since computational cost is exponential. In our framework, rules are operated on outputs of base classifiers which frequently have lower dimensionality than the original data. Moreover, outputs of base classifiers are scaled within the range [0, 1] so it is convenient to apply fuzzy rules directly instead of requiring data transformation and normalization before generating fuzzy rules. The performance of this model was evaluated through experiments on 6 commonly used datasets from UCI Machine Learning Repository and compared with several state-of-art combining classifiers algorithms and fuzzy IF-THEN rules approaches. The results show that our framework can improve the classification accuracy.

External IDs:dblp:conf/icmlc/NguyenLTPN14