Approximative fuzzy rules approaches for classification with hybrid-GA techniques

In this paper the use of different methods from the fuzzy modeling field for classification tasks is evaluated and the potential of their integration in producing better classification results is investigated. The methods considered, approximative in their nature, consider different integrations of techniques with an initial rule generation step and a following rule tuning approach using different evolutionary algorithms. We analyse the adaptation of existing techniques in the fuzzy modeling context for the classification problem, and the integration of these techniques in order to improve the classifiers performance. Finally a genetic algorithm (GA) for translation from approximative rules to similar descriptive ones trying to preserve the accuracy of the approximative classifier is presented. The classical Iris and Cancer data set are used throughout the evaluation process to form a common ground for comparison and performance analysis. (C) 2001 Elsevier Science Inc. All rights reserved.