CMM home site

Overview

If it is to qualify as knowledge, a learner's output should be accurate, stable and comprehensible. Learning multiple models can improve significantly on the accuracy and stability of single models, but at the cost of losing their comprehensibility (when they possess it, as do, for example, simple decision trees and rule sets). This project develops and evaluates CMM, a meta-learner that seeks to retain most of the accuracy gains of multiple model approaches, while still producing a single comprehensible model. CMM is based on reapplying the base learner to recover the frontiers implicit in the multiple model ensemble. This is done by giving the base learner a new training set, composed of a large number of examples generated and classified according to the ensemble, plus the original examples.

Project members

• Pedro Domingos

Publications

• Pedro Domingos. Knowledge Discovery Via Multiple Models. In Intelligent Data Analysis. 1998.
• Pedro Domingos. Why Does Bagging Work? A Bayesian Account and its Implications. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97). 1997.
• Pedro Domingos. Knowledge Acquisition from Examples Via Multiple Models. In Proceedings of the Fourteenth International Conference on Machine Learning (ICML '97). 1997.
• Pedro Domingos. Bayesian Model Averaging in Rule Induction. In Preliminary Papers of the Sixth International Workshop on Artificial Intelligence and Statistics. 1997.