The evaluation of classifier performance in a cost-sensitive setting is straightforward if the
operating conditions (misclassification costs and class distributions) are fixed and known …

Evaluating classifier performance in a cost-sensitive setting is straightforward if the operating
conditions (misclassification costs and class distributions) are fixed and known. When this is …

Research in machine learning, statistics and related fields has produced a wide variety of
algorithms for classification. However, most of these algorithms assume that all errors have …

This paper introduces cost curves, a graphical technique for visualizing the performance
(error rate or expected cost) of 2-class classifiers over the full range of possible class …

This paper addresses two cost-sensitive learning methodology issues. First, we ask the
question of whether Bagging is always an appropriate procedure to compute accurate class …

Performance evaluation plays an important role in the rule induction and classification
process. Classic evaluation measures have been extensively studied in the past. In recent …

In data mining and in classification specifically, cost issues have been undervalued for a
long time, although they are of crucial importance in real-world applications. Recently …

Abstract Machine learning models are increasing in popularity in many domains as they are
shown to be able to solve difficult problems. However, selecting a model to implement when …

Often the accuracy is not adequate as a performance measure of classifiers when costs are
different for different prediction errors. ROC and cost graphs can be used in such case to …

Classification is a well-studied problem in data mining. Classification performance was
originally gauged almost exclusively using predictive accuracy, but as work in the field …