In this paper a hybrid feature selection method is proposed which takes advantages of
wrapper subset evaluation with a lower cost and improves the performance of a group of …

Besides optimizing classifier predictive performance and addressing the curse of the
dimensionality problem, feature selection techniques support a classification model as …

This paper proposes a filter-based method for feature selection. The filter is based on the
partitioning of the feature space into clusters of similar features. The number of clusters and …

Datasets often include excessive or irrelevant data that affect the performance and
complexity of the machine learning model. Feature selection is one of the most effective …

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 is viewed as an important preprocessing step for pattern recognition,
machine learning and data mining. It is used to find an optimal subset to reduce …

HE growth of the size of data and number of existing databases exceeds the ability of
humans to analyze this data, which creates both a need and an opportunity to extract …

In pattern classification, feature selection is an important factor in the performance of
classifiers. In particular, when classifying a large number of features or variables, the …

Feature selection aims at finding a feature subset that has the most discriminative
information from the original feature set. In this paper, we firstly present a new scheme for …

Feature selection techniques have become an obvious need for researchers in computer
science and many other fields of science. Whether the target research is in medicine …