Feature selection has become interest to many research areas which deal with machine
learning and data mining, because it provides the classifiers to be fast, cost-effective, and …

Feature selection methods are deployed in machine-learning algorithms for reducing the
redundancy in the dataset and to increase the clarity in the system models without loss of …

Feature selection has been widely applied in many areas such as classification of spam
emails, cancer cells, fraudulent claims, credit risk, text categorisation and DNA microarray …

Feature selection is the process of eliminating features from the data set that are irrelevant
with respect to the task to be performed. Feature selection is important for many reasons …

Feature Subset Selection is an essential pre-processing task in Data Mining. Feature
selection process refers to choosing subset of attributes from the set of original attributes …

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 …

Abstract±In this paper is presented the impact of feature selection on the accuracy of naïve
Bayes classifier. Six feature selection techniques have been used for feature selection …

Classification of data crosses different domains has been extensively researched and is one
of the basic methods for distinguishing one from another, as we need to know which belongs …

In the paper, we present an empirical evaluation of five feature selection methods: ReliefF,
random forest feature selector, sequential forward selection, sequential backward selection …

A lot of candidate features are usually provided to a learning algorithm for producing a
complete characterization of the classification task. However, it is often the case that majority …