Learning from Imbalanced Data in Presence of Noisy and Borderline Examples

In this paper we studied re-sampling methods for learning classifiers from imbalanced data. We carried out a series of experiments on artificial data sets to explore the impact of noisy and borderline examples from the minority class on the classifier performance. Results showed that if data was sufficiently disturbed by these factors, then the focused re-sampling methods

• Type: Journal
• Year: 2010
• Where: RSCTC
• Authors: Krystyna Napierala, Jerzy Stefanowski, Szymon Wilk
• Keywords: Focused Re-sampling Methods, Fuzzy Logic, Large Overlapping Ares, Noisy Examples, RSCTC 2010