One major component of machine learning is feature analysis which comprises of mainly
two processes: feature selection and feature extraction. Due to its applications in several …

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 …

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 …

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 …

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

Interacting features are those that appear to be irrelevant or weakly relevant with the class
individually, but when it combined with other features, it may highly correlate to the class …

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 …

Feature selection is one of the most important preprocessing steps for a data mining, pattern
recognition or machine learning problem. Finding an optimal subset of features, among all …