This paper reports a new framework for test-cost sensitive classification. It introduces a new
loss function definition, in which misclassification cost and cost of feature extraction are …

We report a novel approach for designing test-cost sensitive classifiers that consider the
misclassification cost together with the cost of feature extraction utilizing the consistency …

Cost sensitive learning is firstly defined as a procedure of minimizing the costs of
classification errors. It has attracted much attention in the last few years. Being cost sensitive …

This chapter introduces cost-sensitive learning and its importance in medicine. Health
managers and clinicians often need models that try to minimize several types of costs …

In the area of cost-sensitive learning, inductive learning algorithms have been extended to
handle different types of costs to better represent misclassification errors. Most of the …

Cost-sensitive classification is one of mainstream research topics in data mining and
machine learning that induces models from data with unbalance class distributions and …

Feature selection and classification are important tasks in medical data mining. However,
different misclassifications of medical cases could lead to different losses. This paper …

Many real-world data mining applications need varying cost for different types of
classification errors and thus call for cost-sensitive classification algorithms. Existing …

The goal of classification with rejection is to avoid risky misclassification in error-critical
applications such as medical diagnosis and product inspection. In this paper, based on the …

In medical diagnosis, doctors often have to order sets of medical tests in sequence in order
to make an accurate diagnosis of patient diseases. While doing so they have to make a …