Selective Pre-processing of Imbalanced Data for Improving Classification Performance

In this paper we discuss problems of constructing classifiers from imbalanced data. We describe a new approach to selective preprocessing of imbalanced data which combines local over-sampling of the minority class with filtering difficult examples from the majority classes. In experiments focused on rule-based and tree-based classifiers we compare our approach with two other related pre-processing methods

• Type: Conference
• Year: 2008
• Where: DAWAK
• Authors: Jerzy Stefanowski, Szymon Wilk
• Keywords: DAWAK 2008, Imbalanced Data, Information Management, Minority Class, Related Pre-processing Methods