Dynamically adaptive and diverse dual ensemble learning approach for handling concept drift in data streams

Concept drift refers to the change in data distributions and evolving relationships between input and output variables with the passage of time. To analyze such variations in learning environments and generate models which can accommodate changing performance of predictive systems is one of the challenging machine learning applications. In general, the majority of the existing schemes consider one of the specific drift types: gradual, abrupt, recurring, or mixed, with traditional voting setup. In this work, we propose a novel data stream framework, dynamically adaptive and diverse dual ensemble (DA-DDE) which responds to multiple drift types in the incoming data streams by combining online and block-based ensemble techniques. In the proposed scheme, a dual diversified ensemble-based system is constructed with the combination of active and passive ensembles, updated over a diverse set of resampled input space. The adaptive weight setting method is proposed in this work which utilizes the overall performance of learners on historic as well as recent concepts of distributions. Further a dual voting system has been used for hypothesis generation by considering dynamic adaptive credibility of ensembles in real time. Comparative analysis with 14 state-of-the-art algorithms on 24 artificial and 11 real datasets shows that DA-DDE is highly effective in handling various drift types.

Automated summary

The article presents a new data stream framework DA-DDE, which responds to multiple drift types by combining online and block-based ensemble techniques, demonstrating high effectiveness in handling various drift types.