Affiliations: [a] Department of Computer, University of Sichuan, Chengdu, Sichuan, China | [b] Information Center, Nanjing Jiangbei People's Hospital, Nanjing, Jiangsu, China
Correspondence:
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Corresponding author: Shenggen Ju, Department of Computer, University of Sichuan, Chengdu, Sichuan 610065, China. Tel.: +86 13382795079; Fax: +86 2557085611; E-mail:gzh669@163.com
Abstract: Rule-learning extracts the knowledge from a dataset and represent it in a
form that is easy for people to understand. RIPPER (Repeated Incremental Pruning to Produce Error Reduction)
and PART (Partial Decision Trees) are two well-known schemes for rule-learning. However, due to overpruning of RIPPER and
skew-sensitivity of PART, it is difficult to use two methods to learn from imbalanced datasets. To bypass these difficulties,
we propose a K-L divergence-based PART (KLPART) that use K-L divergence as a splitting criterion to build partial decision trees.
An experimental framework is carried out with a wide range of imbalanced datasets over RIPPER, PART, KLPART and the combination
of these methods for classification with SMOTE processing. The results obtained, which contrasted through nonparametric
statistical tests, show that KLPART is robust in the presence of class imbalance, especially when combined with SMOTE.
We thereby recommend the use of KLPART with SMOTE when learning from imbalanced datasets.
