Scaling up the accuracy of Naive-Bayes classifiers: A decision-tree hybrid

Conference · Tue Dec 31 04:00:00 EST 1996

OSTI ID:421279

Kohavi, R ^[1]

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1. Silicon Graphics, Inc., Mountain View, CA (United States)

Naive-Bayes induction algorithms were previously shown to be surprisingly accurate on many classification tasks even when the conditional independence assumption on which they are based is violated. However, most studies were done on small databases. We show that in some larger databases, the accuracy of Naive-Bayes does not scale up as well as decision trees. We then propane a new algorithm, NBTree, which induces a hybrid of decision-tree classifiers and Naive-Bayes classifiers; the decision-tree nodes contain univariate splits as regular decision-trees, but the leaves contain Naive-Bayesian classifiers. The approach retains the interpretability of Naive-Bayes and decision trees, while resulting in classifiers that frequently out-perform both constituents, especially in the larger databases tested.

• OSTI ID: 421279
• Report Number(s): CONF-960830--
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS
ACCURACY
ALGORITHMS
ARTIFICIAL INTELLIGENCE
DATA BASE MANAGEMENT
DECISION TREE ANALYSIS
INFORMATION RETRIEVAL
KNOWLEDGE BASE