Abstract. In the paper, a new method of decision tree learning for costsensitive classification is presented. In contrast to the traditional greedy top-down inducer in the proposed...
- 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 occurr...
In this paper we propose a very simple, yet general and effective method to make any cost-insensitive classifiers (that can produce probability estimates) cost-sensitive. The meth...
This paper addresses cost-sensitive classification in the setting where there are costs for measuring each attribute as well as costs for misclassification errors. We show how to ...
In this paper, we study cost-sensitive semi-supervised learning where many of the training examples are unlabeled and different misclassification errors are associated with unequa...