Comparison of training set reduction techniques for Pittsburgh approach Genetic Classifier Systems
نویسندگان
چکیده
In this paper we deal with the problem of reducing the computational cost of a Genetic Based Machine Learning (GBML) system based on the Pittsburgh Approach. In previous work we studied an incremental learning scheme that divided the training set in several strata and changed the used strata at each iteration. This scheme reduced the computational cost more than expected and even managed to improve the accuracy of the system. In this paper we compare our previous scheme with two alternative methods.
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