Parallel Distributed Implementation of Genetics-Based Machine Learning for Fuzzy Classifier Design

Evolutionary algorithms have been successfully applied to design fuzzy rule-based classifiers. They are used for attribute selection, fuzzy set selection, rule selection, membership function tuning, and so on. Genetics-based machine learning (GBML) is one of the promising evolutionary algorithms for classifier design. It can find an appropriate combination of antecedent sets for each rule in a classifier. Although GBML has high search ability, it needs long computation time especially for large data sets. In this paper, we apply a parallel distributed implementation to our fuzzy genetics-based machine learning. In our method, we divide not only a population but also a training data set into subgroups. These subgroups are assigned to CPU cores. Through computational experiments on large data sets, we show the effectiveness of the proposed parallel distributed implementation.

[1]  Hisao Ishibuchi,et al.  Classification and modeling with linguistic information granules - advanced approaches to linguistic data mining , 2004, Advanced information processing.

[2]  Hiroshi Motoda,et al.  Feature Selection for Knowledge Discovery and Data Mining , 1998, The Springer International Series in Engineering and Computer Science.

[3]  Francisco Herrera,et al.  Stratification for scaling up evolutionary prototype selection , 2005, Pattern Recognit. Lett..

[4]  Francisco Herrera,et al.  On the combination of evolutionary algorithms and stratified strategies for training set selection in data mining , 2006, Appl. Soft Comput..

[5]  Dr. Alex A. Freitas.  Data Mining and Knowledge Discovery with Evolutionary Algorithms , 2002, Natural Computing Series.

[6]  Enrique Alba,et al.  Parallelism and evolutionary algorithms , 2002, IEEE Trans. Evol. Comput..

[7]  Huan Liu,et al.  Instance Selection and Construction for Data Mining , 2001 .

[8]  Larry Bull,et al.  Learning Classifier Systems in Data Mining , 2008, Learning Classifier Systems in Data Mining.

[9]  Hisao Ishibuchi,et al.  Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning , 2007, Int. J. Approx. Reason..

[10]  F. Gomide,et al.  Ten years of genetic fuzzy systems: current framework and new trends , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[11]  Hisao Ishibuchi,et al.  Parallel distributed genetic fuzzy rule selection , 2008, Soft Comput..

[12]  Francisco Herrera,et al.  Genetic fuzzy systems: taxonomy, current research trends and prospects , 2008, Evol. Intell..

[13]  Hisao Ishibuchi,et al.  Use of very small training data subsets in parallel distributed genetic fuzzy rule selection , 2010, 2010 4th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS).

[14]  Hisao Ishibuchi,et al.  Selecting fuzzy if-then rules for classification problems using genetic algorithms , 1995, IEEE Trans. Fuzzy Syst..

[15]  Hisao Ishibuchi,et al.  Hybridization of fuzzy GBML approaches for pattern classification problems , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[16]  Hisao Ishibuchi,et al.  Effects of Data Reduction on the Generalization Ability of Parallel Distributed Genetic Fuzzy Rule Selection , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

[17]  Erick Cantú-Paz,et al.  A Survey of Parallel Genetic Algorithms , 2000 .