A reward-directed Bayesian classifier

Li Hui, Liao Xuejun, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


We consider a classification problem wherein the class features are not given a priori. The classifier is responsible for selecting the features, to minimize the cost of observing features while also maximizing the classification performance. We propose a reward-directed Bayesian classifier (RDBC) to solve this problem. The RDBC features an internal state structure for preserving the feature dependence, and is formulated as a partially observable Markov decision process (POMDP). The results on a diabetes dataset show the RDBC with a moderate number of states significantly improves over the naive Bayes classifier, both in prediction accuracy and observation parsimony. It is also demonstrated that the RDBC performs better by using more states to increase its memory. © 2006 IEEE.
Original languageEnglish (US)
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
StatePublished - Dec 1 2006
Externally publishedYes

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