MuSeRA: Multiple Selectively Recursive Approach towards imbalanced stream data mining

Learning from data streams has inspired considerable interests in recent years due to its wide applications in the fields such as network intrusion detection, credit fraud identification, spam filtering, and many others. Given the fact that most algorithms developed thus far assume the class distribution of the streaming data is relatively balanced, they will inevitably be confronted with severe performance deterioration when handling the imbalanced data streams. Evolved from our previous work SERA (SElectively Recursive Approach), the MuSeRA algorithm is proposed in this paper to deal with the problem of learning from imbalanced data streams. By maintaining an ensemble consisting of hypotheses built upon the coming training data chunks balanced by selectively accommodating previous minority examples, MuSeRA can efficiently learn the target concept of the imbalanced data streams and thus obtain substantial performance improvement compared to our previous work SERA and the existing stream data mining algorithms. Simulation results validate the effectiveness of the proposed MuSeRA algorithm.