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-performanc...

Ausführliche Beschreibung