MOB-ESP and other improvements in probability estimation

Authors:
Rodney D. Nielsen
Affiliations:
University of Colorado, Boulder, CO
Venue:
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Year:
2004

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Abstract

A key prerequisite to optimal reasoning under uncertainty in intelligent systems is to start with good class probability estimates. This paper improves on the current best probability estimation trees (Bagged-PETs) and also presents a new ensemble-based algorithm (MOB-ESP). Comparisons are made using several benchmark datasets and multiple metrics. These experiments show that MOB-ESP outputs significantly more accurate class probabilities than either the baseline B-PETs algorithm or the enhanced version presented here (EB-PETs). These results are based on metrics closely associated with the average accuracy of the predictions. MOB-ESP also provides much better probability rankings than B-PETs. The paper further suggests how these estimation techniques can be applied in concert with a broader category of classifiers.