Data stream mining is very popular in recent years with advanced electronic devices generating continuous data streams. The performance of standard learning algorithms is been compromised with imbalance nature present in real-world data streams. In this paper we propose a novel algorithm dubbed as Incremental Fusion Sampling for Data Streams (IFSDS) which uses a unique over sampling and under sampling techniques to almost balance the data sets to minimize the effect of imbalance in the stream mining process. The experimental analysis is conducted on 10 data chunks of data streams with varied sizes and different imbalance ratios. The results suggest that the proposed IFSDS algorithm improves the knowledge discovery over benchmark algorithms like C4.5 and Hoeffding tree in terms of performance measures TN Rate, FP Rate, precision, and F-measure.

Knowledge Discovery, Data Streams, Imbalanced data, oversampling, under sampling, Increment Fusion Sampling for Data Streams (IFSDS)

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