… 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, …

… 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. …

… 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 …

… 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. …

… However, indiscernibility or weak indiscernibility relations are qualitative. Thus, the similarity
relation is employed to provide a quantitative description of the indiscernibility. For nominal …

… cost-sensitive learning. In this paper, we propose a cost-sensitive hierarchical classification
… sensitive weights accordingly. First, we use the class hierarchy to divide a large hierarchical …

… Forest classification and introduce a completely new feature selection idea. Additionally,
we also develop cost-sensitive adaptations for existing popular methods in this context. …

… This approach is often referred to as cost-based feature selection or Test-cost-sensitive
Feature Selection (TCSFS). For instance, Min et al. (2011) proposed an information gain-based …