LAUSR

Abstract In the diversity of contemporary decision-making tasks, where the data is no longer static and changes over time, data stream processing has become an important issue in the field of pattern recognition. In addition, most of the real problems are not balanced, representing their classes in various improportions. Following paper proposes the Prior Imbalance Compensation method, modifying on-the-fly predictions made by the base classifier, aiming at mapping prior probability in the statistics of assigned classes. It is intended to be a less computationally complex competition for popular algorithms such as smote , solving this problem by oversampling the training set. The proposed method has been tested using computer experiments on the example of a set of various data streams, leading to promising results, suggesting its usefulness in solving this type of problems.