Cost-sensitive learning has become an increasingly important area that recognizes that real world classification problems need to take the costs of misclassification and accuracy into account. Much work has been done on cost-sensitive decision tree learning, but very little has been done on cost-sensitive Bayesian networks. Although there has been significant research on Bayesian networks there...

The classifier built from a data set with a highly skewed class distribution generally predicts the more frequently occurring classes much more often than the infrequently occurring classes. This is largely due to the fact that most classifiers are designed to maximize accuracy. In many instances, such as for medical diagnosis, this classification behavior is unacceptable because the minority c...

We consider analysis of data from an unmatched case-control study design with a binary genetic factor and a binary environmental exposure when both genetic and environmental exposures could be potentially misclassified. We devise an estimation strategy that corrects for misclassification errors and also exploits the gene-environment independence assumption. The proposed corrected point estimate...

In diagnostic decision-support systems, test selection amounts to selecting, in a sequential manner, a test that is expected to yield the largest decrease in the uncertainty about a patient’s diagnosis. For capturing this uncertainty, often an information measure is used. In this paper, we study the Shannon entropy, the Gini index, and the misclassification error for this purpose. We argue that...

Abstract We examine economic mobility in India while accounting for misclassification to better understand the welfare effects of rise inequality. To proceed, we extend recently developed methods on partial identification transition matrices. Allowing modest misclassification, find overall has been remarkably low: at least 65% poor households remained or at-risk being between 2005 and 2012. als...