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Survey on Feature Selection for Data Reduction

by R. K. Bania
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 94 - Number 18
Year of Publication: 2014
Authors: R. K. Bania
10.5120/16456-2390

R. K. Bania . Survey on Feature Selection for Data Reduction. International Journal of Computer Applications. 94, 18 ( May 2014), 1-7. DOI=10.5120/16456-2390

@article{ 10.5120/16456-2390,
author = { R. K. Bania },
title = { Survey on Feature Selection for Data Reduction },
journal = { International Journal of Computer Applications },
issue_date = { May 2014 },
volume = { 94 },
number = { 18 },
month = { May },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume94/number18/16456-2390/ },
doi = { 10.5120/16456-2390 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:17:59.334981+05:30
%A R. K. Bania
%T Survey on Feature Selection for Data Reduction
%J International Journal of Computer Applications
%@ 0975-8887
%V 94
%N 18
%P 1-7
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The storage capabilities and advanced in data collection has led to an information load and the size of databases increases in dimensions, not only in rows but also in columns. Data reduction (DR) plays a vital role as a data prepossessing techniques in the area of knowledge discovery from the huge collection of data. Feature selection (FS) is one of the well known data reduction techniques, which deals with the reduction of attributes from the original data without affecting the main information content. Based on the training data used for different applications of knowledge discovery, FS technique falls into supervised, unsupervised. In this paper an extensive survey on supervised FS technique describing the different searching approach, methods and application areas with an outline of a comparative study is covered.

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Index Terms

Computer Science
Information Sciences

Keywords

Data reduction Feature selection Filter Wrapper Embedded

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