Diversity in task decomposition: A strategy for combining mixtures of experts

The “no free lunch” theorem has stated that learning algorithms cannot be universally good. An alternative to alleviate the weakness of using only one classifier is to combine several of them. Mixture of Experts is a learning algorithm that combines classifiers, in which each classifier or expert is dedicated to solve part of the problem. The partition of the problem is defined by a step called Task Decomposition where the problem is divided in subproblems. This paper proposes an approach to combine mixture of experts, in which different task decomposition methods are used to divide the problem. This strategy aims to increase the diversity of the ensemble, since different task decomposition methods generate different partitions of the database. The experimental study shows that the proposed method obtains better accuracy rates when compared with the traditional mixture of experts.