Boosting for Learning Multiple Classes with Imbalanced Class Distribution

Classification of data with imbalanced class distribution has posed a significant drawback of the performance attainable by most standard classifier learning algorithms, which assume a relatively balanced class distribution and equal misclassification costs. This learning difficulty attracts a lot of research interests. Most efforts concentrate on bi-class problems. However, bi-class is not the only scenario where the class imbalance problem prevails. Reported solutions for bi-class applications are not applicable to multi-class problems. In this paper, we develop a cost-sensitive boosting algorithm to improve the classification performance of imbalanced data involving multiple classes. One barrier of applying the cost-sensitive boosting algorithm to the imbalanced data is that the cost matrix is often unavailable for a problem domain. To solve this problem, we apply Genetic Algorithm to search the optimum cost setup of each class. Empirical tests show that the proposed cost-sen...

• Type: Conference
• Year: 2006
• Where: ICDM
• Authors: Yanmin Sun, Mohamed S. Kamel, Yang Wang 0007
• Keywords: Cost-sensitive Boosting Algorithm, Data Mining, ICDM 2006, Imbalanced Class Distribution, Imbalanced Data