An experimental study on feature subset selection methods

Chulmin Yun; Donghyuk Shin; Hyunsung Jo; Jihoon Yang; Saejoon Kim

doi:10.1109/CIT.2007.4385060

An experimental study on feature subset selection methods

Chulmin Yun, Donghyuk Shin, Hyunsung Jo, Jihoon Yang, Saejoon Kim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

19 Scopus citations

Abstract

In the field of machine learning and pattern recognition, feature subset selection is an important area, where many approaches have been proposed. In this paper, we choose some feature selection algorithms and analyze their performance using various datasets from public domain. We measured the number of reduced features and the improvement of learning performance with chosen feature selection methods, then evaluated and compared each method on the basis of these measurements.

Original language	English (US)
Title of host publication	CIT 2007
Subtitle of host publication	7th IEEE International Conference on Computer and Information Technology
Pages	77-82
Number of pages	6
DOIs	https://doi.org/10.1109/CIT.2007.4385060
State	Published - 2007
Externally published	Yes
Event	CIT 2007: 7th IEEE International Conference on Computer and Information Technology - Aizu-Wakamatsu, Fukushima, Japan Duration: Oct 16 2007 → Oct 19 2007

Publication series

Name	CIT 2007: 7th IEEE International Conference on Computer and Information Technology

Conference

Conference	CIT 2007: 7th IEEE International Conference on Computer and Information Technology
Country/Territory	Japan
City	Aizu-Wakamatsu, Fukushima
Period	10/16/07 → 10/19/07

ASJC Scopus subject areas

Computer Science Applications
Information Systems
Software
General Mathematics

Access to Document

10.1109/CIT.2007.4385060

Cite this

An experimental study on feature subset selection methods. / Yun, Chulmin; Shin, Donghyuk; Jo, Hyunsung et al.
CIT 2007: 7th IEEE International Conference on Computer and Information Technology. 2007. p. 77-82 4385060 (CIT 2007: 7th IEEE International Conference on Computer and Information Technology).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yun, C, Shin, D, Jo, H, Yang, J & Kim, S 2007, An experimental study on feature subset selection methods. in CIT 2007: 7th IEEE International Conference on Computer and Information Technology., 4385060, CIT 2007: 7th IEEE International Conference on Computer and Information Technology, pp. 77-82, CIT 2007: 7th IEEE International Conference on Computer and Information Technology, Aizu-Wakamatsu, Fukushima, Japan, 10/16/07. https://doi.org/10.1109/CIT.2007.4385060

@inproceedings{e4181f903c36452a8c5301fcddc892a3,

title = "An experimental study on feature subset selection methods",

abstract = "In the field of machine learning and pattern recognition, feature subset selection is an important area, where many approaches have been proposed. In this paper, we choose some feature selection algorithms and analyze their performance using various datasets from public domain. We measured the number of reduced features and the improvement of learning performance with chosen feature selection methods, then evaluated and compared each method on the basis of these measurements.",

author = "Chulmin Yun and Donghyuk Shin and Hyunsung Jo and Jihoon Yang and Saejoon Kim",

note = "Copyright: Copyright 2008 Elsevier B.V., All rights reserved.; CIT 2007: 7th IEEE International Conference on Computer and Information Technology ; Conference date: 16-10-2007 Through 19-10-2007",

year = "2007",

doi = "10.1109/CIT.2007.4385060",

language = "English (US)",

isbn = "0769529836",

series = "CIT 2007: 7th IEEE International Conference on Computer and Information Technology",

pages = "77--82",

booktitle = "CIT 2007",

}

TY - GEN

T1 - An experimental study on feature subset selection methods

AU - Yun, Chulmin

AU - Shin, Donghyuk

AU - Jo, Hyunsung

AU - Yang, Jihoon

AU - Kim, Saejoon

PY - 2007

Y1 - 2007

N2 - In the field of machine learning and pattern recognition, feature subset selection is an important area, where many approaches have been proposed. In this paper, we choose some feature selection algorithms and analyze their performance using various datasets from public domain. We measured the number of reduced features and the improvement of learning performance with chosen feature selection methods, then evaluated and compared each method on the basis of these measurements.

AB - In the field of machine learning and pattern recognition, feature subset selection is an important area, where many approaches have been proposed. In this paper, we choose some feature selection algorithms and analyze their performance using various datasets from public domain. We measured the number of reduced features and the improvement of learning performance with chosen feature selection methods, then evaluated and compared each method on the basis of these measurements.

UR - http://www.scopus.com/inward/record.url?scp=38049014391&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=38049014391&partnerID=8YFLogxK

U2 - 10.1109/CIT.2007.4385060

DO - 10.1109/CIT.2007.4385060

M3 - Conference contribution

AN - SCOPUS:38049014391

SN - 0769529836

SN - 9780769529837

T3 - CIT 2007: 7th IEEE International Conference on Computer and Information Technology

SP - 77

EP - 82

BT - CIT 2007

T2 - CIT 2007: 7th IEEE International Conference on Computer and Information Technology

Y2 - 16 October 2007 through 19 October 2007

ER -

An experimental study on feature subset selection methods

Abstract

Publication series

Conference

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this