… 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 …

… Usually the cost for the sequence of testing is denoted as test cost, while the cost of delaying
action on a decision is denoted as delay cost. Table 4 offers an example of delay costs. …

… data classification. Three classification costs, namely misclassification cost, test cost and …
Under the framework of semi-supervised learning, this paper introduces a cost-sensitive …

… costs such as the attribute cost (eg, the diagnostic test cost) and the misclassification cost
(eg, the false negative test cost). They show that the diagnostic tests and the misclassification …

… attracted by the test cost in recent years. The researches of test-cost-sensitive learning in …
set model based on test cost, and the other is test-cost sensitive based attribute reduction. As …

… regarding the test costs during the test cost sensitive attribute reduction process, we propose
an … into the test cost sensitive decision model and establish a basis for quantitative analysis. …