A novel neuro-fuzzy method for linguistic feature selection and rule-based classification

This paper proposes a new interpretable neuro-fuzzy classification mechanism. The proposed neuro-fuzzy structure is different from other data analysis mechanisms previously invented in pattern recognition. General mechanisms focus mainly on creating predictive data models whereas some useful information inside the process may be ignored. The proposed mechanism is designed based on the consideration of feature selection and rule extraction. It is a three-layer feedforward network. Its structure can be comprehended to logical rules using only selected important features. We construct a new classification algorithm by using a small number of features that represent an informative subset of a given dataset. This classifier can produce good classification results from the direct calculation or from logical rule extraction. Pleasant performance of classification results are acquired from 10-fold cross validation testing on several standard datasets.

[1]  Ignacio Requena,et al.  Are artificial neural networks black boxes? , 1997, IEEE Trans. Neural Networks.

[2]  M. Teshnehlab,et al.  Hierarchical Takagi-Sugeno type fuzzy system for diabetes mellitus forecasting , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[3]  Nikhil R. Pal,et al.  A neuro-fuzzy scheme for simultaneous feature selection and fuzzy rule-based classification , 2004, IEEE Transactions on Neural Networks.

[4]  Jim Esch.  Computational Intelligence Methods For Rule-Based Data Understanding , 2004, Proc. IEEE.

[5]  S. Mitra,et al.  Neurofuzzy classification and rule generation of modes of radiowave propagation , 2003 .

[6]  Michael Margaliot,et al.  Are artificial neural networks white boxes? , 2005, IEEE Transactions on Neural Networks.

[7]  Seyed Mostafa Fakhrahmad,et al.  Efficient Fuzzy Rule Generation: A New Approach Using Data Mining Principles and Rule Weighting , 2007, Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007).

[8]  LiMin Fu,et al.  Rule Generation from Neural Networks , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[9]  Saroj K. Meher,et al.  A novel approach to neuro-fuzzy classification , 2009, Neural Networks.

[10]  LiMin Fu.  Learning capacity and sample complexity on expert networks , 1996, IEEE Trans. Neural Networks.

[11]  José Manuel Benítez,et al.  Interpretation of artificial neural networks by means of fuzzy rules , 2002, IEEE Trans. Neural Networks.

[12]  Jacek M. Zurada,et al.  Computational intelligence methods for rule-based data understanding , 2004, Proceedings of the IEEE.

[13]  De-xian Zhang,et al.  Effectively extracting rules from trained neural networks based on the characteristics of the classification hypersurfaces , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[14]  Sushmita Mitra,et al.  Neuro-fuzzy rule generation: survey in soft computing framework , 2000, IEEE Trans. Neural Networks Learn. Syst..

[15]  Dezhao Chen,et al.  Fuzzy Information Granulation Based Decision Support Applications , 2008, 2008 International Symposiums on Information Processing.

[16]  Li-Min Fu.  Knowledge-based connectionism for revising domain theories , 1993, IEEE Trans. Syst. Man Cybern..

[17]  Jude W. Shavlik,et al.  Extracting Refined Rules from Knowledge-Based Neural Networks , 1993, Machine Learning.

[18]  Rudy Setiono.  Extracting M-of-N rules from trained neural networks , 2000, IEEE Trans. Neural Networks Learn. Syst..

[19]  C. Lursinsap,et al.  Critical support vector machine without kernel function , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..