Abstract
When using machine learning to solve real world problems, the class distribution used in the training set is important; not only in highly unbalanced data sets but in every data set. Weiss and Provost suggested that each domain has an optimal class distribution to be used for training. The aim of this work was to analyze the truthfulness of this hypothesis in the context of decision tree learners. With this aim we found the optimal class distribution for 30 databases and two decision tree learners, C4.5 and Consolidated Tree Construction algorithm (CTC), taking into account pruned and unpruned trees and based on two measures for evaluating discriminating capacity: AUC and error. The results confirmed that changes in the class distribution of the training samples improve the performance (AUC and error) of the classifiers. Therefore, the experimentation showed that there is an optimal class distribution for each database and this distribution depends on the used learning algorithm, whether the trees are pruned or not and the used evaluation criteria. Besides, results showed that CTC algorithm combined with optimal class distribution samples achieves more accurate learners, than any of the options of C4.5 and CTC with original distribution, with statistically significant differences.
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Albisua, I. et al. (2010). Obtaining Optimal Class Distribution for Decision Trees: Comparative Analysis of CTC and C4.5. In: Meseguer, P., Mandow, L., Gasca, R.M. (eds) Current Topics in Artificial Intelligence. CAEPIA 2009. Lecture Notes in Computer Science(), vol 5988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14264-2_11
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DOI: https://doi.org/10.1007/978-3-642-14264-2_11
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