Improving the Reliability of Decision Tree and Naive Bayes Learners

Authors:
David Lindsay;Sian Cox
Affiliations:
University of London, UK;University of London, UK
Venue:
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Year:
2004

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Abstract

The C4.5 Decision Tree and Naive Bayes learners are known to produce unreliable probability forecasts. We have used simple Binning and Laplace Transform techniques to improve the reliability of these learners and compare their effectiveness with that of the newly developed Venn Probability Machine (VPM) meta-learner. We assess improvements in reliability using loss functions, Receiver Operator Characteristic (ROC) curves and Empirical Reliability Curves (ERC). The VPM outperforms the simple techniques to improve reliability, although at the cost of increased computational intensity and slight increase in error rate. These trade-offs are discussed.