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Bayes Vector Quantizer for Class-Imbalance Problem
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DOI：10.1109/TKDE.2008.187.png)
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摘要
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The class-imbalance problem is the problem of learning a classification rule from data that are skewed in favor of one class. On these data sets, traditional learning techniques tend to overlook the less numerous classes, at the advantage of the majority class. However, the minority class is often the most interesting one for the task at hand. For this reason, the class-imbalance problem has received increasing attention in the last few years. In the present paper, we point the attention of the reader to a learning algorithm for the minimization of the average misclassification risk. In contrast to some popular class-imbalance learning methods, this method has its roots in statistical decision theory. A particular interesting characteristic is that when class distributions are unknown, the method can work by resorting to stochastic gradient algorithm. We study the behavior of this algorithm on imbalanced data sets, demonstrating that this principled approach allows to obtain better classification performances compared to the principal methods proposed in the literature.
Keyword:
Class imbalance
labeled vector quantizer
average misclassification risk minimization
cost-sensitive learning
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