Adjusted Probability Naive Bayesian Induction

Naive Bayesian classi ers utilise a simple mathematical model for induction. While it is known that the assumptions on which this model is based are frequently violated, the predictive accuracy obtained in discriminate classi cation tasks is surprisingly competitive in comparison to more complex induction techniques. Adjusted probability naive Bayesian induction adds a simple extension to the naive Bayesian classi er. A numeric weight is inferred for each class. During discriminate classi cation, the naive Bayesian probability of a class is multiplied by its weight to obtain an adjusted value. The use of this adjusted value in place of the naive Bayesian probability is shown to signi cantly improve predictive accuracy.

• Type: Conference
• Year: 1998
• Where: AUSAI
• Authors: Geoffrey I. Webb, Michael J. Pazzani
• Keywords: Artificial Intelligence, AUSAI 1998, Bayesian Classi Er, Discriminate Classi Cation, Naive Bayesian Classi