Fuzzy If-Then Rules Classifier on Ensemble Data
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This paper introduces a novel framework in which classification method based on fuzzy IF-THEN rules is co-operated with an ensemble system. This model tackles several drawbacks. First, IF-THEN rules approaches have problem with high dimensional data since computational cost is exponential function. In this framework, rules are operated on outputs of base classifiers which have frequently lower dimension than 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 data transformation and normalization as requirement for inputs of fuzzy rules. The performance of this model was evaluated through experiments on 6 popular datasets from UCI Machine Learning Repository and comparisons with other state-of-art combining classifiers algorithms as well as other fuzzy IF-THEN rules approaches. The results show that our framework can improve the classification accuracy.
Proceedings of the 13th International Conference on Machine Learning and Cybernetics
© 2014 Springer Berlin/Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.com
Neural, Evolutionary and Fuzzy Computation