Variable and feature selection have become the focus of much research in areas of
application for which datasets with tens or hundreds of thousands of variables are available …

The attribute selection techniques for supervised learning, used in the preprocessing phase
to emphasize the most relevant attributes, allow making models of classification simpler and …

Feature selection is an important problem for pattern classifier systems. As compared to
unsupervised feature selection methods, supervised feature selection approaches have …

In supervised learning scenarios, feature selection has been studied widely in the literature.
Selecting features in unsupervised learning scenarios is a much harder problem, due to the …

The use of feature selection can improve accuracy, efficiency, applicability and
understandability of a learning process and the resulting learner. For this reason, many …

Feature selection aims to choose a feature subset that has the most discriminative
information from the original feature set. In practical cases, it is preferable to select a feature …

The performance of classification algorithms in machine learning is affected by the features
used to describe the labeled examples presented to the inducers. Therefore, the problem of …

The problem of feature selection is to find a subset of features for optimal classification. A
critical part of feature selection is to rank features according to their importance for …

It has been our experience that in order to obtain useful results using supervised learning of
real-world datasets it is necessary to perform feature subset selection and to perform many …

Attribute selection is a data preprocessing step which aims at identifying relevant attributes
for the target machine learning task–namely classification in this paper. In this paper, we …