In many data mining and machine learning applications, there are two objectives in the task
of classification; one is decreasing the test cost, the other is improving the classification …

Feature selection is an important preprocessing step in machine learning and data mining.
In real-world applications, costs, including money, time and other resources, are required to …

Attribute reduction is a key issue in rough set theory which is widely used to handle
uncertain knowledge. However, most existing attribute reduction approaches focus on cost …

This paper deals with the problem of computing the shortest reducts of a decision system.
The shortest reducts are useful for attribute reduction in classification problems and data …

Feature selection is an essential process in data mining applications since it reduces a
model's complexity. However, feature selection with various types of costs is still a new …

Test costs and misclassification costs are two most important types in cost-sensitive learning.
In decision systems with both costs, there is a tradeoff between them while building a …

Cost-sensitive feature selection learning is an important preprocessing step in machine
learning and data mining. Recently, most existing cost-sensitive feature selection algorithms …

Test-cost-sensitive attribute reduction is an important component in data mining
applications, and plays a key role in cost-sensitive learning. Some previous approaches in …

Data streams are ubiquitous and related to the proliferation of low-cost mobile devices,
sensors, wireless networks and the Internet of Things. While it is well known that complex …

In many machine learning applications, we need to pay test cost for each data item. Due to
limited money and/or time, we also have a constraint on the total test cost. This issue have …