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A Characterization of the Combined Effects of Overlap and Imbalance on the SVM Classifier

Authors
  • Denil, Misha
  • Trappenberg, Thomas
Type
Preprint
Publication Date
Sep 16, 2011
Submission Date
Sep 16, 2011
Identifiers
arXiv ID: 1109.3532
Source
arXiv
License
Yellow
External links

Abstract

In this paper we demonstrate that two common problems in Machine Learning---imbalanced and overlapping data distributions---do not have independent effects on the performance of SVM classifiers. This result is notable since it shows that a model of either of these factors must account for the presence of the other. Our study of the relationship between these problems has lead to the discovery of a previously unreported form of "covert" overfitting which is resilient to commonly used empirical regularization techniques. We demonstrate the existance of this covert phenomenon through several methods based around the parametric regularization of trained SVMs. Our findings in this area suggest a possible approach to quantifying overlap in real world data sets.

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