… matrix is reasonable, and we show how to avoid the mistake of defining a cost … cost-sensitive
classification decisions using a classifier learned by a standard non-costsensitive learning …

… In this chapter we will discuss the basics of cost-sensitive methods, introduce their taxonomy…
cost is not given beforehand by an expert. Then we will describe most popular cost-sensitive …

… In Section 5 we show an example, how can the cost sensitive neural learning be used in
the ROC analysis of the real-world medical problem. Section 6 gives some conclusions and …

… We summarize the theory of cost-sensitive learning, … cost plays its essential role in various
cost-sensitive learning … In cost-sensitive learning, the costs of false positive (actual negative …

… different learning algorithms to accomplish costsensitive classification. We call this the
transparent box approach because it requires knowledge of the particular learning algorithm (as …

… involving 20 multi-class data sets and seven types of cost-sensitive learners validates our
proposal. Moreover, we show that the proposal is also helpful for class-imbalance learning. …

… this problem, including resampling techniques and costsensitive learning (CSL), most of them
… The second method proposes using CSL by optimizing the cost ratio (cost matrix) locally. …

… -cost misclassification, and thus make the total misclassification losses minimized. To deal
with this issue, cost-sensitive learning … theoretical foundation of cost-sensitive learning as the …

… for learning-fromexamples methods that trade off accuracy for efficiency during learning, …
Moreover, this article illustrates the application of such a cost-sensitive-learning method to a …

… Although both cost-sensitive learning and privacy preserving are two actively pursued goals
… generic framework that can learn cost-sensitive learning algorithms with privacy guarantee …