Integrating selective pre-processing of imbalanced data with Ivotes ensemble

Authors:
Jerzy Błaszczyński;Magdalena Deckert;Jerzy Stefanowski;Szymon Wilk
Affiliations:
Institute of Computing Science, Poznań University of Technology, Poznań, Poland;Institute of Computing Science, Poznań University of Technology, Poznań, Poland;Institute of Computing Science, Poznań University of Technology, Poznań, Poland;Institute of Computing Science, Poznań University of Technology, Poznań, Poland
Venue:
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Year:
2010

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Abstract

In the paper we present a new framework for improving classifiers learned from imbalanced data. This framework integrates the SPIDER method for selective data pre-processing with the Ivotes ensemble. The goal of such integration is to obtain improved balance between the sensitivity and specificity for the minority class in comparison to a single classifier combined with SPIDER, and to keep overall accuracy on a similar level. The IIvotes framework was evaluated in a series of experiments, in which we tested its performance with two types of component classifiers (tree- and rule-based). The results show that IIvotes improves evaluation measures. They demonstrated advantages of the abstaining mechanism (i.e., refraining from predictions by component classifiers) in IIvotes rule ensembles.