Universal target learning: An efficient and effective technique for semi-naive Bayesian learning

Abstract

To mitigate the negative effect of classification bias caused by overfitting, semi-naive Bayesian techniques seek to mine the implicit dependency relationships in unlabeled testing instances. By redefining some criteria from information theory, Target Learning (TL) proposes to build for each unlabeled testing instance P the Bayesian Network Classifier BNCP, which is independent and complementary to BNCT learned from training data T . In this paper, we extend TL to Universal Target Learning (UTL) to identify redundant correlations between attribute values and maximize the bits encoded in the Bayesian network in terms of log likelihood. We take the k-dependence Bayesian classifier as an example to investigate the effect of UTL on BNCP and BNCT . Our extensive experimental results on 40 UCI datasets show that UTL can help BNC improve the generalization performance.