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 …

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 …

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

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 is a type of learning in data mining that takes the misclassification
costs (and possibly other types of cost) into consideration. The goal of this type of learning is …

In real-world applications the number of examples in one class may overwhelm the other
class, but the primary interest is usually on the minor class. Cost-sensitive learning has been …

Cost-sensitive learning is an aspect of algorithm-level modifications for class imbalance.
Here, instead of using a standard error-driven evaluation (or 0–1 loss function), a …

Rescaling is possibly the most popular approach to cost‐sensitive learning. This approach
works by rebalancing the classes according to their costs, and it can be realized in different …

Cost-sensitive learning is a popular approach to addressing the problem of class imbalance
for many classification algorithms in machine learning. However, most cost-sensitive …

In data mining and in classification specifically, cost issues have been undervalued for a
long time, although they are of crucial importance in real-world applications. Recently …