A Novel Class Imbalance Learning Method using Subset Filtering

In many real-world applications, the problem of learning from imbalanced data (the imbalanced learningproblem) is a relatively new challenge that has attracted growing attention from both academia and industry. The imbalanced learning problem is concerned with the performance of learning algorithms in the presence of underrepresented data and severe class distribution skews. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledgerepresentation.In this paper, we present a new hybrid subset filtering approach for learning from skewed trainingdata. This algorithm provides a simpler and faster alternative by using C4.5 as base algorithm. We conduct experiments usingeleven UCI data sets from various application domains using f0ur base learners,and five evaluation metrics. Experimentalresults show that our method has higher Area under the ROC Curve, F-measure, precision, TP rate and TN rate values than many existing class imbalance learning methods.