… is expressed as a precise quantitative value that is selected … importance in terms of precise
quantitative values. Instead, they … In this paper, we report a novel test-cost sensitive approach …

… with the classification accuracy issue. We focus on the test cost issue, with the classification
… This metric is both qualitative and quantitative. First, it only counts optimal solutions. Second…

… With this framework, we present a test cost sensitive decision tree model with two kinds of
cost scales. The task is to minimize one cost scale, called target cost, and keep the other one …

… Therefore we focus on comparison of ICET with algorithms that are sensitive to test cost (IDX,
CS-ID3, and EG2). In future work, we will examine complex cost matrices and compare …

… This metric is both qualitative and quantitative. First, it only counts optimal solutions. Second…
of a test-costsensitive attribute reduction. We formally defined the minimal test cost reduct …

… algorithm and is based on the inclusion of test cost as well as misclassification cost in the …
While discretizing a quantitative attribute, FFD predefines a sufficient interval frequency k …

… This metric is both qualitative and quantitative, because it only counts optimal solutions
and it is based on a number of searches. In the experiments, different test cost settings have …

… We design an active learning algorithm for cost-sensitive multiclass classification: problems
… and test cost on real-world datasets. Keywords: Active Learning, Cost-sensitive Learning, …

… , such as Pawlak’s rough set, quantitative rough set and decision-theoretic rough set in …
take less care about the test cost, then we would decrease the weight of test cost. In general, …

… paper describes the integration of qualitative and quantitative criteria into a unified general
… We define the cost-sensitive attribute reduction by examining both the test cost function and …