A Study with Class Imbalance and Random Sampling for a Decision Tree Learning System

Sampling methods are a direct approach to tackle the problem of class imbalance. These methods sample a data set in order to alter the class distributions. Usually these methods are applied to obtain a more balanced distribution. An open-ended question about sampling methods is which distribution can provide the best results, if any. In this work we develop a broad empirical study aiming to provide more insights into this question. Our results suggest that altering the class distribution can improve the classification performance of classifiers considering AUC as a performance metric. Furthermore, as a general recommendation, random over-sampling to balance distribution is a good starting point in order to deal with class imbalance.

• Type: Conference
• Year: 2008
• Where: IFIP12
• Authors: Ronaldo C. Prati, Gustavo E. A. P. A. Batista, Maria Carolina Monard
• Keywords: Class Distribution, Class Imbalance, IFIP12 2008, Information Technology, Sampling Methods