Improving Accuracy and Cost of Two-class and Multi-class
Probabilistic Classifiers Using ROC Curves  Nicolas Lachiche and
Peter Flach  The probability estimates of a naive Bayes
classifier are inaccurate if some of its underlying independence
assumptions are violated. The decision criterion for using these
estimates for classification therefore has to be learned from
the data. This paper proposes the use of ROC curves for this
purpose. For two classes, the algorithm is a simple adaptation
of the algorithm for tracing a ROC curve by sorting the
instances according to their predicted probability of being
positive. As there is no obvious way to upgrade this algorithm
to the multi-class case, we propose a hillclimbing approach
which adjusts the weights for each class in a pre-defined order.
Experiments on a wide range of datasets show the proposed method
leads to significant improvements over the naive Bayes
classifier’s accuracy. Finally, we discuss an method to find the
global optimum, and show how its computat