Imbalanced Data Stream Classification Assisted by Prior Probability Estimation

Joanna Komorniczak, Pawel Zyblewski, Pawel Ksieniewicz

Abstract: With the processing of data streams, come inevitable challenges, such as changes in the prior (class drift) and posterior (concept drift) probability distribution over the processing time. Both these phenomena have a negative impact on the quality of the classification. Heavily imbalanced problems, which are often typical for real-world applications, bring additional processing difficulties. Classifiers are often biased towards the majority class and have difficulty identifying instances of categories described with a lower number of objects. The following article proposes a Prior Probability Assisted Classifier (2PAC), a method aiming to improve the classification quality of heavily imbalanced data streams with dynamic changes by using the estimated prior probability value and the correction of the classifier's decision for batch predictions. Presented extensive computer experiments, supported by statistical analysis, show the ability to improve the classification quality using the proposed method.