Noise Reduction A Priori Synthetic Over-Sampling for class imbalanced data sets

In real world data set the underlying data distribution may be highly skewed. Building accurate classifiers for predicting group membership is made difficult because the classifier has a tendency to be biased towards the over represented or majority group as a result. This problem is referred to as a class imbalance problem. Re-sampling techniques that produce new samples by means of over-sampling aim to combat class imbalance by increasing the number of members that belong to the minority group. This paper introduces a new over-sampling technique that focuses on noise reduction and selective sampling of the minority group which results in improvement for prediction of minority group membership. Experiments are conducted across a wide range of data sets, learners and over sampling methods. The results for this new method show improvement for Sensitivity and Gmean measures over the compared approaches.