A novel approach using incremental oversampling for data stream mining

Abstract

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 increment over sampling for data streams (IOSDS) which uses an unique over sampling technique to almost balance the data sets to minimize the effect of imbalance in stream mining process. The experimental analysis is conducted on 15 data chunks of data streams with varied sizes and different imbalance ratios. The results suggests that the proposed IOSDS 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 and F-measure.