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

TL;DR

The paper introduces the Nataf-Beta Random Field Classifier, a novel probabilistic model extending Beta priors to classification, suitable for continuous and integer attributes, with competitive accuracy on benchmark datasets.

Contribution

It extends Beta conjugate priors to classification by modeling class probabilities as random fields, enabling a new flexible discriminative approach.

Findings

Ranks among top classifiers on benchmark datasets.

Suitable for real-continuous and real-integer attributes.

Does not statistically outperform the best methods, but shows competitive accuracy.

Abstract

This paper presents the Nataf-Beta Random Field Classifier, a discriminative approach that extends the applicability of the Beta conjugate prior to classification problems. The approach's key feature is to model the probability of a class conditional on attribute values as a random field whose marginals are Beta distributed, and where the parameters of marginals are themselves described by random fields. Although the classification accuracy of the approach proposed does not statistically outperform the best accuracies reported in the literature, it ranks among the top tier for the six benchmark datasets tested. The Nataf-Beta Random Field Classifier is suited as a general purpose classification approach for real-continuous and real-integer attribute value problems.