Bayes Vector Quantizer for Class-Imbalance Problem

• Published in: IEEE transactions on knowledge and data engineering Vol. 21; no. 5; pp. 638 - 651
• Main Authors: Diamantini, C., Potena, D.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.05.2009; IEEE Computer Society; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; and association rules; Applied sciences; Artificial intelligence; Classification; Classifier design and evaluation; Clustering; Computer science; control theory; systems; Costs; Data mining; Data processing. List processing. Character string processing; Decision theory; Exact sciences and technology; Learning; Learning systems; Machine learning; Mathematical analysis; Medical diagnosis; Memory organisation. Data processing; Minimization methods; Mining methods and algorithms; Optimization; Probability; Readers; Risk management; Software; Stochastic processes; Studies; Tasks; Vectors (mathematics); and association rules; Mining methods and algorithms; Machine learning; Clustering; classification; Data mining; Classifier design and evaluation; Gradient; Vector quantization; Class imbalance; cost-sensitive learning; labeled vector quantizer; Minimization; Statistical decision; Randomized algorithm; average misclassification risk minimization; Search algorithm; Decision theory; Classification; Learning algorithm; Artificial intelligence; Skewed distribution
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2008.187

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.