Bayesian probability

Pattern recognition (psychology)

Gibbs sampling

Oversampling

Bandwidth (computing)

Computer network

Mathematics

Markov chain

Markov chain Monte Carlo

Statistics

Filter (signal processing)

Machine learning

Probability distribution

Sampling (signal processing)

Resampling

Joint probability distribution

Artificial intelligence

Class (philosophy)

Computer science

Computer vision

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.

wRACOG: A Gibbs Sampling-Based Oversampling Technique