Predicting Concept Changes Using a Committee of Experts

In on-line machine learning, predicting changes is not a trivial task. In this paper, a novel prediction approach is presented, that relies on a committee of experts. Each expert is trained on a specific history of changes and tries to predict future changes. The experts are constantly modified based on their performance and the committee as a whole is thus dynamic and can adapt to a large variety of changes. Experimental results based on synthetic data show three advantages: (a) it can adapt to different types of changes, (b) it can use different types of prediction models and (c) the committee outperforms predictors trained on a priori fixed size history of changes.