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 …

Feature selection is an indispensable step in the data preprocessing stage of data mining
and pattern recognition. In some numerical small sample data, it is often high dimensional in …

Feature selection refers to a problem to select a subset of features which are most optimal
for intended tasks. As one of well-known feature selection methods, clustering features into …

This paper introduces a wrapper method, namely cosine similarity measure support vector
machines (CSMSVM), to eliminate irrelevant or redundant features during classifier …

The process of Feature selection in machine learning involves the reduction in the number
of features (genes) and similar activities that results in an acceptable level of classification …

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 …

This article presents an analysis of data extraction for classification using correlation
coefficient and fuzzy model. Several traditional methods of data extraction are used for …

Feature selection is an important data preprocessing technique and has been widely
studied in data mining, machine learning and granular computing. In this paper, we …

In today's world large amount of data is available in science, industry, business and many
other areas. These data can provide valuable information which can be used by …

Feature Selection (FS) is an imperative issue in data mining and machine learning. It is an
inevitable task to shorter the number of features presented in the initial data set for better …