A novel approach using incremental under sampling for data stream mining
N Anupama, Sudarson Jena · Big Data and Information Analytics · 2017
Data stream mining is every popular in recent years with advanced electronic devices generating continuous data streams. The performance of standard learning algorithms has been compromised with imbalance nature present in real world data streams. In this paper, we propose an algorithm known as Increment Under Sampling for Data streams (IUSDS) which uses an unique under sampling technique to almost balance the data sets to minimize the effect of imbalance in stream mining process. The experimental analysis conducted suggests that the proposed algorithm improves the knowledge discovery over benchmark algorithms like C4.5 and Hoeffding tree in terms of standard performance measures namely accuracy, AUC, precision, recall, F-measure, TP rate, FP rate and TN rate.