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 …

In the area of cost-sensitive learning, inductive learning algorithms have been extended to
handle different types of costs to better represent misclassification errors. Most of the …

In data mining applications, we have a number of measurement methods to obtain a data
item with different test costs and different error ranges. Test costs refer to time, money, or …

In the field of rough set, how to improve the efficiency of obtaining reduct has been paid
much attention to. One of the typical strategies is to reduce the number of comparisons …

Minimal cost classification is an important issue in data mining and machine learning.
Recently, many enhanced algorithms based on the C4. 5 algorithm have been proposed to …

By considering the information granulation in Granular Computing, the concept of the multi-
granularity is important. It is mainly because different results of information granulation will …

Feature selection is an optional preprocessing procedure and is frequently used to improve
the classification accuracy of a machine learning algorithm by removing irrelevant and/or …

In medical diagnosis, doctors often have to order sets of medical tests in sequence in order
to make an accurate diagnosis of patient diseases. While doing so they have to make a …

Many real-world data sets for machine learning and data mining contain missing values and
much previous research regards it as a problem and attempts to impute missing values …

In rough set theory, the concept of conditional entropy has been widely accepted for
studying the problem of attribute reduction. If a searching strategy is given to find reduct …