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The performance of classification algorithms with a massive and highlyimbalanced data stream depends upon efficient balancing strategy. Sometechniques of balancing strategy have been applied in the past with Batch datato resolve the class imbalance problem. This paper proposes a new incrementaldata balancing framework which can work with massive imbalanced data streams.In this paper, we choose Racing Algorithm as an automated data balancingtechnique which optimizes the balancing techniques. We applied Random Forestclassification algorithm which can deal with the massive data stream. Weinvestigated the suitability of Racing Algorithm and Random Forest in theproposed framework. Applying new technique in the proposed framework on theEuropean Credit Card dataset, provided better results than the Batch mode. Theproposed framework is more scalable to handle online massive data streams.

Improving fraud prediction with incremental data balancing technique for massive data streams