Mixing independent classifiers

In this study we deal with the mixing problem, which concerns combining the prediction of independently trained local models to form a global prediction. We deal with it from the perspective of Learning Classifier Systems where a set of classifiers provide the local models. Firstly, we formalise the mixing problem and provide both analytical and heuristic approaches to solving it. The analytical approaches are shown to not scale well with the number of local models, but are nevertheless compared to heuristic models in a set of function approximation tasks. These experiments show that we can design heuristics that exceed the performance of the current state-of-the-art Learning Classifier System XCS, and are competitive when compared to analytical solutions. Additionally, we provide an upper bound on the prediction errors for the heuristic mixing approaches.

• Type: Conference
• Year: 2007
• Where: GECCO
• Authors: Jan Drugowitsch, Alwyn Barry
• Keywords: GECCO 2007, Learning Classifier System, Local Models, Mixing Problem, Optimization