Utilizing DTRS for imbalanced text classification

Abstract

Imbalanced data classification is one of the challenging problems in data mining and machine learning research. The traditional classification algorithms are often biased towards the majority class when learning from imbalanced data. Much work have been proposed to address this problem, including data re-sampling, algorithm modification, and cost-sensitive learning. However, most of them focus on one of these techniques. This paper proposes to utilize both algorithm modification and cost-sensitive learning based on decision-theoretic rough set (DTRS) model. In particular, we use naive Bayes classifier as the base classifier and modify it for imbalanced learning. For cost-sensitive learning, we adopt the systematic method from DTRS to derive required thresholds that have the minimum decision cost. Our experimental results on three well-known text classification databases show that unified DTRS provides similar performance on balanced class distribution, outperforms naive Bayes classifier on imbalanced datasets, and is competitive with other imbalanced learning classifier.