Abstract
This paper presents the performance evaluation of the recently developed Sequential Adaptive Fuzzy Inference System (SAFIS) algorithm for classification problems. In SAFIS the number of fuzzy rules can be automatically determined according to learning process and the parameters in fuzzy rules can be updated simultaneously. Earlier SAFIS has been evaluated only for function approximation problems. Improvements to SAFIS for enhancing its performance in both accuracy and speed are described in the paper and the resulting algorithm is referred to as Extended SAFIS (ESAFIS). In ESAFIS, the concept of the modified influence of a fuzzy rule is introduced for adding or removing the fuzzy rules. If the input data does not warrant adding of fuzzy rules, the parameters of the fuzzy rules are updated using a Recursive Least Square Error (RLSE) scheme. Empirical study of ESAFIS is executed based on several commonly used classification benchmark problems. The results indicate that the proposed ESAFIS produces higher classification accuracy with reduced computational complexity compared with original SAFIS and other algorithms, such as eTS (Angelov and Filev 2004), Simpl_eTS (Angelov and Filev 2005) and k-NN (Huang et al. 2006).
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Acknowledgments
This work is funded in part by National Natural Science Foundation of China (Grant No. 61004055); Shaanxi Provincial Natural Science Foundation of China (Grant No. 2009JQ8003); and the Scientific Research Plan for New Teachers, Xi’an Jiaotong University. The authors would like to thank the anonymous reviewers for their invaluable suggestions which have been incorporated to improve the quality of the paper.
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Rong, HJ., Sundararajan, N., Huang, GB. et al. Extended sequential adaptive fuzzy inference system for classification problems. Evolving Systems 2, 71–82 (2011). https://doi.org/10.1007/s12530-010-9023-9
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DOI: https://doi.org/10.1007/s12530-010-9023-9