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Issue No. 05 - May (2009 vol. 21)
ISSN: 1041-4347
pp: 638-651
Claudia Diamantini , Università Politecnica delle Marche, Ancona
Domenico Potena , Università Politecnica delle Marche, Ancona
The class-imbalance problem is the problem of learning a classification rule from data that are skewed in favor of one class. On these datasets traditional learning techniques tend to overlook the less numerous class, 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 datasets, demonstrating that this principled approach allows to obtain better classification performances compared to the principal methods proposed in the literature.
Clustering, classification, and association rules, Data mining, Mining methods and algorithms, Machine learning, Classifier design and evaluation

D. Potena and C. Diamantini, "Bayes Vector Quantizer for Class-Imbalance Problem," in IEEE Transactions on Knowledge & Data Engineering, vol. 21, no. , pp. 638-651, 2008.
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