Estimating The Potential for Combining Learning Models

This research describes the COD (Classifier Output Difference) distance metric for finding similarity between hypotheses and learning algorithms. This metric is a tool which can be used to measure how similar two hypotheses are. It goes beyond simple accuracy comparisons and provides insights about fundamental differences between learning models. This paper describes how COD works and shows how it can be used to predict the potential for combining hypotheses in an ensemble to improve accuracy. 1. Measuring hypothesis similarity