Feature selection goal is to get rid of redundant and irrelevant features. The problem of
feature subset selection is that of finding a subset of the original features of a dataset, such …

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 selection is the process of identifying relevant features in the dataset and discarding
everything else as irrelevant and redundant. Since feature selection reduces the …

Feature selection is an important part in any of the data processing algorithms as it reduces
the complexity of the processor by the reduction of the feature space. In this paper we …

This paper proposes one method of feature selection by using Bayes' theorem. The purpose
of the proposed method is to reduce the computational complexity and increase the …

Authors here tried to use the WEKA tool to evaluate the performance of various classifiers on
a dataset to come out with the optimum classifier, for a particular application. A Classifier is …

The high dimensionality problem, which is one of the problems encountered in classification
problems, arises when there are too many features in the dataset. This problem affects the …

Dimensionality problem is a well-known challenging issue for most classifiers in which
datasets have unbalanced number of samples and features. Features may contain …

Sažetak Aiming to deal with the irrelevant or redundant features, this paper proposes eight
kinds of feature selection methods. The first seven feature selection methods include CART …