A Memetic Approach for the Knowledge Extraction
This paper provides a memetic approach in order to extract comprehensible and accurate classification rules. Indeed to construct a model of classification we need to extract not only accurate rules but comprehensible also, to help the human interpretation of the model and the decision make process. In this paper we describe a purely genetic approach, then a tabu search approach and finaly a memetic algorithm to extract classification rules. The memetic approach is a hybridization of a genetic algorithm and a local search based on a tabu search algorithm. Many conducted tests on the well-known UCI (University of California Irvine) benchmarks are presented and a comparative study of the obtained results with the three approaches is also presented. The paper will conclude by giving a discussion on the obtained results for the three approaches and some future works.
KeywordsClassification Extraction of Rules Memetic Algorithm Genetic Algorithm Tabu Search Algorithm Michigan Approach
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