Cost-Sensitive Pattern-Based classification for Class Imbalance problems

Abstract

In several problems, contrast pattern-based classifiers produce high accuracy and provide an explanation of the result in terms of the patterns used for classification. However, class imbalance problems are a great challenge for these classifiers because there exist significantly fewer objects belonging to a class regarding the remaining classes and this biases the classification to the majority class. Therefore, in this paper, we propose an algorithm for discovering cost-sensitive patterns in class imbalance problems and a pattern-based classifier which uses these patterns for classification. Our proposal follows the idea of fusing pattern discovery with the cost-sensitive approach for class imbalance problems. Our experiments show that our proposal obtains cost-sensitive patterns, which allow attaining significantly lower misclassification cost than using patterns mined by other well-known state-of-the-art pattern miners. Also, we show that our proposed pattern-based classifier is suitable for working with cost-sensitive patterns.