Class Imbalance, Redux | IEEE Conference Publication | IEEE Xplore

Abstract:

Class imbalance (i.e., scenarios in which classes are unequally represented in the training data) occurs in many real-world learning tasks. Yet despite its practical impo...Show More

Abstract:

Class imbalance (i.e., scenarios in which classes are unequally represented in the training data) occurs in many real-world learning tasks. Yet despite its practical importance, there is no established theory of class imbalance, and existing methods for handling it are therefore not well motivated. In this work, we approach the problem of imbalance from a probabilistic perspective, and from this vantage identify dataset characteristics (such as dimensionality, sparsity, etc.) that exacerbate the problem. Motivated by this theory, we advocate the approach of bagging an ensemble of classifiers induced over balanced bootstrap training samples, arguing that this strategy will often succeed where others fail. Thus in addition to providing a theoretical understanding of class imbalance, corroborated by our experiments on both simulated and real datasets, we provide practical guidance for the data mining practitioner working with imbalanced data.
Date of Conference: 11-14 December 2011
Date Added to IEEE Xplore: 23 January 2012
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Conference Location: Vancouver, BC, Canada

References

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