Weighting and sampling data for individual classifiers and bagging with genetic algorithms

An imbalanced or inappropriate dataset can have a negative influence in classification model training. In this paper we present an evolutionary method that effectively weights or samples the tuples from the training dataset and tries to minimize the negative effects from innaprotirate datasets. The genetic algorithm with genotype of real numbers is used to evolve the weights or occurrence number for each learning tuple in the dataset. This technique is used with individual classifiers and in combination with the ensemble technique of bagging, where multiple classification models work together in a classification process. We present two variations - weighting the tuples and sampling the classification tuples. Both variations are experimentally tested in combination with individual classifiers (C4.5 and Naive Bayes methods) and in combination with bagging ensemble. Results show that both variations are promising techniques, as they produced better classification models than methods without weighting or sampling, which is also supported with statistical analysis.