A novel framework for class imbalance learning using intelligent under-sampling

Abstract

In data mining and Knowledge Discovery hidden and valuable knowledge from the data sources is discovered. The traditional algorithms used for knowledge discovery are bottle necked due to wide range of data sources availability. Class imbalance is one of the problems arises due to data source which provide unequal class, i.e., examples of one class in a training dataset vastly outnumber examples of the other class(es). Researchers have rigorously studied several techniques to alleviate the problem of class imbalance, including resampling algorithms, and feature selection approaches to this problem. In this paper, we present a new hybrid frame work and two algorithms dubbed as Class Imbalance Learning using Intelligent Under Sampling—Tree and Neural Network versions (CILIUS-T, CILIUS-NN) for learning from skewed training data. These algorithms provide a simpler and faster alternative by using C4.5 and Neural Network as base algorithm. We conduct experiments using ten UCI datasets from various application domains using five algorithms for comparison on five evaluation metrics. Experimental results 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.