wRACOG: A Gibbs Sampling-Based Oversampling Technique
Barnan Das, Narayanan Chatapuram Krishnan, Diane J. Cook · 2013
As machine learning techniques mature and are used to tackle complex scientific problems, challenges arise such as the imbalanced class distribution problem, where one of the target class labels is under-represented in comparison with other classes. Existing over sampling approaches for addressing this problem typically do not consider the probability distribution of the minority class while synthetically generating new samples. As a result, the minority class is not well represented which leads to high misclassification error. We introduce wRACOG, a Gibbs sampling-based over sampling approach to synthetically generating and strategically selecting new minority class samples. The Gibbs sampler uses the joint probability distribution of data attributes to generate new minority class samples in the form of a Markov chain. wRACOG iteratively learns a model by selecting samples from the Markov chain that have the highest probability of being misclassified. We validate the effectiveness of wRACOG using five UCI datasets and one new application domain dataset. A comparative study of wRACOG with three other well-known resampling methods provides evidence that wRACOG offers a definite improvement in classification accuracy for minority class samples over other methods.