In machine learning, feature selection is preprocessing step and can be effectively reduce high dimensional data, remove irrelevant data, increase learning accuracy, and improve result comprehensibility. High dimensionality of data take over efficiency and effectiveness points of view in feature selection algorithm. Efficiency stands required time to find a subset of features, and the effective...

Gene expressions by microarray data technique have been effectively utilized for classification and diagnostic of cancer nodules. Numerous data mining techniques like clustering are presently applied for identifying cancer using gene expression data. An unsupervised learning technique is a clustering technique used to find out grouping structure in a set of data. The problem of feature selectio...

in this paper we propose a new method for classification of subjects into schizophrenia and control groups using functional magnetic resonance imaging (fmri) data. in the preprocessing step, the number of fmri time points is reduced using principal component analysis (pca). then, independent component analysis (ica) is used for further data analysis. it estimates independent components (ics) of...

In this paper, the problem of classifying HTML documents is investigated in the context of a client-server application, named WebClass, developed to support the search activity of a geographically distributed group of people with common interests. The two main issues studied in the paper are the selection of some features to represent HTML documents and the construction of the classifiers. A ne...

The Clustering is a method of grouping the information into modules or clusters. Their dimensionality increases usually with a tiny number of dimensions that are significant to definite clusters, but data in the unrelated dimensions may produce much noise and wrap the actual clusters to be exposed. Attribute subset selection method is frequently used for data reduction through removing unrelate...

Analysis of DNA sequences isolated directly from the environment, known as metagenomics, produces a large quantity of genome fragments that need to be classified into specific taxa. Most composition-based classification methods use all features instead of a subset of features that may maximize classifier accuracy. We show that feature selection methods can boost performance of taxonomic classif...

Different approaches have been proposed for feature selection to obtain suitable features subset among all features. These methods search feature space for feature subsets which satisfies some criteria or optimizes several objective functions. The objective functions are divided into two main groups: filter and wrapper methods.  In filter methods, features subsets are selected due to some measu...