Attribute reduction is a typical topic in the field of rough sets. As an extension of this topic,
test cost sensitive attribute reduction has garnered considerable attention in recent years …

As a generalization of the classical reduct problem, test-cost-sensitive attribute reduction
aims at finding a minimal test-cost reduct. The performance of an existing algorithm is not …

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 …

In real applications of data mining, test costs often occur, and numerical data exists widely.
In recent years, based on neighborhood rough set, some algorithms have been proposed to …

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 …

The measurement error with normal distribution is universal in applications. Generally,
smaller measurement error requires better instrument and higher test cost. In decision …

Attribute reduction is a fundamental problem in rough set theory and serves as an effective
data reduction technique. The minimal test cost attribute reduction problem poses a more …

Recent research in machine learning and data mining has produced a wide variety of
algorithms for cost-sensitive learning. Most existing rough set methods on this issue deal …

Cost sensitivity is an important problem, which has been addressed by many researchers
around the world. As far as cost sensitivity in the rough set theory is concerned, two types of …

Attribute reduction is viewed as one of the most important topics in rough set theory and
there have been many researches on this issue. In the real world, partially labeled data is …