Aiming at reducing the total cost in cost-sensitive learning, this paper introduces a semi-
supervised learning model based on uncertainty of sample outputs. Its central idea is (1) to …

The authors briefly review the theory of cost-sensitive learning, and the existing cost-
sensitive learning algorithms. The authors categorize cost-sensitive learning algorithms into …

In many real world data mining and classification tasks, we face with the problem of high
cost in making training data sets. In addition, in many domains, different misclassification …

Cost-sensitive learning is one of the active research topics in data mining and machine
learning, designed for dealing with the non-uniform cost of misclassification errors. In the last …

We propose a bisection method for varying classification threshold value for cost sensitive
neural network learning. Using simulated data and different misclassification cost …

Cost-sensitive learning addresses the issue of classification in the presence of varying costs
associated with different types of misclassification. In this paper, we present a method for …

Cost-sensitive learning has been a hot research topic in machine learning. Many cost-
sensitive methods have been successfully applied in many real-world applications such as …

Many studies on the cost-sensitive learning assumed that a unique cost matrix is known for a
problem. However, this assumption may not hold for many real-world problems. For …

Cost-sensitive learning has received increased attention in recent years. However, in
existing studies, most of the works are devoted to make decision trees cost-sensitive and …

Existing cost-sensitive learning methods require that the unequal misclassification costs
should be given as precise values. In many real-world applications, however, it is generally …