One-sided prototype selection on class imbalanced dissimilarity matrices

Authors:
Mónica Millán-Giraldo;Vicente García;J. Salvador Sánchez
Affiliations:
Intelligent Data Analysis Laboratory, University of Valencia, Burjassot, Valencia, Spain,Institute of New Imaging Technologies, Department of Computer Languages and Systems, University Jaume I, Ca ...;Institute of New Imaging Technologies, Department of Computer Languages and Systems, University Jaume I, Castelló de la Plana, Spain;Institute of New Imaging Technologies, Department of Computer Languages and Systems, University Jaume I, Castelló de la Plana, Spain
Venue:
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2012

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Abstract

In the dissimilarity representation paradigm, several prototype selection methods have been used to cope with the topic of how to select a small representation set for generating a low-dimensional dissimilarity space. In addition, these methods have also been used to reduce the size of the dissimilarity matrix. However, these approaches assume a relatively balanced class distribution, which is grossly violated in many real-life problems. Often, the ratios of prior probabilities between classes are extremely skewed. In this paper, we study the use of renowned prototype selection methods adapted to the case of learning from an imbalanced dissimilarity matrix. More specifically, we propose the use of these methods to under-sample the majority class in the dissimilarity space. The experimental results demonstrate that the one-sided selection strategy performs better than the classical prototype selection methods applied over all classes.