Building Useful Models from Imbalanced Data with Sampling and Boosting
Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano · 2008
Building useful classification models can be a challeng-ing endeavor, especially when training data is imbal-anced. Class imbalance presents a problem when tra-ditional classification algorithms are applied. These al-gorithms often attempt to build models with the goal of maximizing overall classification accuracy. While such a model may be very accurate, it is often not very useful. Consider the domain of software quality pre-diction where the goal is to identify program modules that are most likely to contain faults. Since these mod-ules make up only a small fraction of the entire project, a highly accurate model may be generated by classi-fying all examples as not fault prone. Such a model would be useless. To alleviate the problems associated with class imbalance, several techniques have been pro-posed. We examine two such techniques: data sampling and boosting. Five data sampling techniques and one commonly used boosting algorithm are applied to five datasets from the software quality prediction domain. Our results suggest that while data sampling can be very effective at improving classification performance when training data is imbalance, boosting (which has received considerably less attention in research related to mining imbalanced data) usually results in even better perfor-mance.