Knowledge discovery from imbalanced and noisy data

Class imbalance and labeling errors present significant challenges to data mining and knowledge discovery applications. Some previous work has discussed these important topics, however the relationship between these two issues has not received enough attention. Further, much of the previous work in this domain is fragmented and contradictory, leading to serious questions regarding the reliability and validity of the empirical conclusions. In response to these issues, we present a comprehensive suite of experiments carefully designed to provide conclusive, reliable, and significant results on the problem of learning from noisy and imbalanced data. Noise is shown to significantly impact all of the learners considered in this work, and a particularly important factor is the class in which the noise is located (which, as discussed throughout this work, has very important implications to noise handling). The impacts of noise, however, vary dramatically depending on the learning algorithm and simple algorithms such as naive Bayes and nearest neighbor learners are often more robust than more complex learners such as support vector machines or random forests. Sampling techniques, which are often used to alleviate the adverse impacts of imbalanced data, are shown to improve the performance of learners built from noisy and imbalanced data. In particular, simple sampling techniques such as random undersampling are generally the most effective.