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This paper presents the Nataf-Beta Random Field Classifier, a discriminativeapproach that extends the applicability of the Beta conjugate prior toclassification problems. The approach's key feature is to model the probabilityof a class conditional on attribute values as a random field whose marginalsare Beta distributed, and where the parameters of marginals are themselvesdescribed by random fields. Although the classification accuracy of theapproach proposed does not statistically outperform the best accuraciesreported in the literature, it ranks among the top tier for the six benchmarkdatasets tested. The Nataf-Beta Random Field Classifier is suited as a generalpurpose classification approach for real-continuous and real-integer attributevalue problems.

The Nataf-Beta Random Field Classifier: An Extension of the Beta Conjugate Prior to Classification Problems