Pattern classification with principal component analysis and fuzzy rule bases

For the first time, the principal component analysis has been used to reduce the feature space dimension in fuzzy rule based pattern classifiers. A modified threshold accepting algorithm proposed elsewhere by V. Ravi and H.-J. Zimmermann has been used to minimize the number of rules in the classifier while guaranteeing high classification power. The proposed methodology has been demonstrated for (1) the wine classification problem, which has 13 features and (2) the Wisconsin breast cancer determination problem, which has 9 features. The influence of the type of aggregator used in the classification algorithm and the number of partitions used for each of the feature spaces is also studied. In conclusion, the results are encouraging as there is no reduction in the classification power in both the problems, despite the fact that some of the principal components have been deleted from the study before invoking the classifier. On the contrary, however, the first five principal components in both the problems yielded 100% classification power in some cases. The high classification power obtained for both the problems while working with reduced feature space dimension is the significant outcome of this study.