Fuzzy logic and evolutionary algorithm - two techniques in rule extraction from neural networks

In this paper, the REX method of fuzzy rule extraction from neural networks (NN) is presented. It is based on evolutionary algorithms. In the search process of the evolutionary algorithm, a set of rules describing the performance of the NN is found. An evolutionary algorithm is also responsible for obtaining proper fuzzy sets. Two approaches are compared, namely REX Pitt and REX Michigan. The main difference lies in the information contained in one chromosome. In REX Pitt, one individual represents a set of rules, while in REX Michigan it represents one rule. The obtained results are compared to other known methods. REX Pitt has very good efficiency, producing a small number of fuzzy rules, while REX Michigan creates more low quality rules.

[1]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[2]  Rudolf Kruse,et al.  Obtaining interpretable fuzzy classification rules from medical data , 1999, Artif. Intell. Medicine.

[3]  Antonio González Muñoz,et al.  Including a simplicity criterion in the selection of the best rule in a genetic fuzzy learning algorithm , 2001, Fuzzy Sets Syst..

[4]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[5]  Ronald J. Patton,et al.  Interpretation of Trained Neural Networks by Rule Extraction , 2001, Fuzzy Days.

[6]  Wlodzislaw Duch,et al.  A new methodology of extraction, optimization and application of crisp and fuzzy logical rules , 2001, IEEE Trans. Neural Networks.

[7]  Joydeep Ghosh,et al.  Symbolic Interpretation of Artificial Neural Networks , 1999, IEEE Trans. Knowl. Data Eng..

[8]  Yufei Yuan,et al.  A genetic algorithm for generating fuzzy classification rules , 1996, Fuzzy Sets Syst..

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

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

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

[12]  H. Levent Akin,et al.  Rule extraction from trained neural networks using genetic algorithms , 1997 .

[13]  J. Paredis,et al.  Rule induction with a genetic sequential covering algorithm (GeSeCo) , 2000 .

[14]  Moshe Sipper,et al.  Fuzzy CoCo: a cooperative-coevolutionary approach to fuzzy modeling , 2001, IEEE Trans. Fuzzy Syst..

[15]  Ching-Chang Wong,et al.  Rule extraction for fuzzy modeling , 1997, Fuzzy Sets Syst..

[16]  C. Matthews,et al.  Fuzzy rule extraction from a trained multilayer neural network , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[17]  Ilona Jagielska,et al.  An investigation into the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition for classification problems , 1999, Neurocomputing.

[18]  Tzung-Pei Hong,et al.  Processing individual fuzzy attributes for fuzzy rule induction , 2000, Fuzzy Sets Syst..

[19]  Joachim Diederich,et al.  Survey and critique of techniques for extracting rules from trained artificial neural networks , 1995, Knowl. Based Syst..

[20]  Urszula Markowska-Kaczmar,et al.  Rule Extraction from Trained Neural Network with Evolutionary Algorithms , 2003 .