Constructing accuracy and diversity ensemble using Pareto-based multi-objective learning for evolving data streams

Ensemble learning is one of the most frequently used techniques for handling concept drift, which is the greatest challenge for learning high-performance models from big evolving data streams. In this paper, a Pareto-based multi-objective optimization technique is introduced to learn high-performance base classifiers. Based on this technique, a multi-objective evolutionary ensemble learning scheme, named Pareto-optimal ensemble for a better accuracy and diversity (PAD), is proposed. The approach aims to enhance the generalization ability of ensemble in evolving data stream environment by balancing the accuracy and diversity of ensemble members. In addition, an adaptive window change detection mechanism is designed for tracking different kinds of drifts constantly. Extensive experiments show that PAD is capable of adapting to dynamic change environments effectively and efficiently in achieving better performance.

Automated summary

Ensemble learning is used to tackle concept drift in big evolving data streams. A Pareto-based multi-objective optimization technique is introduced in this paper to learn high-performance base classifiers, leading to the proposal of a multi-objective evolutionary ensemble learning scheme named PAD. The approach aims to enhance ensemble generalization in an evolving data stream environment by balancing accuracy and diversity, and includes an adaptive window change detection mechanism for tracking different drifts.