Hybrid dynamic chunk ensemble model for multi-class data streams

Abstract

In the analysis more specifically in the classification of continuous data stream using machine learning algorithms joint occurrence of concept drift and imbalanced issue becomes more provocative. Also, imbalance issue is again more challenging when the data stream is multi-class with minority class and that is too with data-difficulty factors. Incremental learning with ensemble models found more promising in handling theses issues. But most of the approaches are for two-class data streams which can’t be utilized for multiclass data streams. In this paper we have designed hybrid dynamic chunk ensemble model (HDCEM) for the classification of multi-class insect-data stream for handling imbalance and concept drift issue. To deal with imbalance issue we have proposed effective split bagging algorithm which has achieved better performance on minority class recall and F-measure on arriving dynamic chunks of data from multi-class data stream. HDCEM model can adapt to abrupt and gradual drift because it has combined features of both online and chunk-based learning together. It has achieved average 78% minority class recall in abrupt insect data stream and 71% in gradual drift insect stream.