Diabetes is an ailment in which glucose level increase in at high rates in blood due to body's
inability to metabolize it. This happens when body does not produce sufficient amount of …

Feature selection aims to select a feature subset from an original feature set based on a
certain evaluation criterion. Since feature selection can achieve efficient feature reduction, it …

Classical fuzzy rough set often uses fuzzy rough dependency as an evaluation function of
feature selection. However, this function only retains the maximum membership degree of a …

Pattern recognition and machine learning techniques have been increasingly adopted in
adversarial settings such as spam, intrusion, and malware detection, although their security …

Attribute reduction is one of the most important preprocessing steps in machine learning and
data mining. As a key step of attribute reduction, attribute evaluation directly affects …

In real-applications, there may exist many kinds of data (eg, boolean, categorical, real-
valued and set-valued data) and missing data in an information system which is called as a …

Feature selection methods with antinoise performance are effective dimensionality reduction
methods for classification tasks with noise. However, there are few studies on robust feature …

Attribute reduction with many patterns and indicators has been regarded as an important
approach for largescale data mining and machine learning tasks. However, it is extremely …

The k-nearest-neighbor rule is a popular classification technique, and rough set theory is an
effective mathematical tool to deal with the uncertainty of data. Rough set models based on k …

Outlier detection is one of the most important research directions in data mining. However,
most of the current research focuses on outlier detection for categorical or numerical …