Local cost sensitive learning for handling imbalanced data sets

Many real-world data sets exhibit skewed class distributions in which almost all cases are allotted to a class and far fewer cases to a smaller, usually more interesting class. A classifier induced from an imbalanced data set has, typically, a low error rate for the majority class and an unacceptable error rate for the minority class. This paper firstly provides a systematic study on the various methodologies that have tried to handle this problem. Finally, it presents an experimental study of these methodologies with a proposed local cost sensitive technique and it concludes that such a framework can be a more effective solution to the problem. Our method seems to allow improved identification of difficult small classes in predictive analysis, while keeping the classification ability of the other classes in an acceptable level.