Great deluge algorithm for rough set attribute reduction

Salwani Abdullah, Najmeh Sadat Jaddi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Citations (Scopus)

Abstract

Attribute reduction is the process of selecting a subset of features from the original set of features that forms patterns in a given dataset. It can be defined as a process to eliminate redundant attributes and at the same time is able to avoid any information loss, so that the selected subset is sufficient to describe the original features. In this paper, we present a great deluge algorithm for attribute reduction in rough set theory (GD-RSAR). Great deluge is a meta-heuristic approach that is less parameter dependent. There are only two parameters needed; the time to "spend" and the expected final solution. The algorithm always accepts improved solutions. The worse solution will be accepted if it is better than the upper boundary value or "level". GD-RSAR has been tested on the public domain datasets available in UCI. Experimental results on benchmark datasets demonstrate that this approach is effective and able to obtain competitive results compared to previous available methods. Possible extensions upon this simple approach are also discussed.

Original languageEnglish
Title of host publicationCommunications in Computer and Information Science
Pages189-197
Number of pages9
Volume118 CCIS
DOIs
Publication statusPublished - 2010
Event2010 International Conferences on Database Theory and Application, DTA 2010 and Bio-Science and Bio-Technology, BSBT 2010, Held as Part of the 2nd International Mega-Conference on Future Generation Information Technology, FGIT 2010 - Jeju Island
Duration: 13 Dec 201015 Dec 2010

Publication series

NameCommunications in Computer and Information Science
Volume118 CCIS
ISSN (Print)18650929

Other

Other2010 International Conferences on Database Theory and Application, DTA 2010 and Bio-Science and Bio-Technology, BSBT 2010, Held as Part of the 2nd International Mega-Conference on Future Generation Information Technology, FGIT 2010
CityJeju Island
Period13/12/1015/12/10

Fingerprint

Rough set theory

Keywords

  • Attribute Reduction
  • Great Deluge
  • Rough Set

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Abdullah, S., & Jaddi, N. S. (2010). Great deluge algorithm for rough set attribute reduction. In Communications in Computer and Information Science (Vol. 118 CCIS, pp. 189-197). (Communications in Computer and Information Science; Vol. 118 CCIS). https://doi.org/10.1007/978-3-642-17622-7_19

Great deluge algorithm for rough set attribute reduction. / Abdullah, Salwani; Jaddi, Najmeh Sadat.

Communications in Computer and Information Science. Vol. 118 CCIS 2010. p. 189-197 (Communications in Computer and Information Science; Vol. 118 CCIS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abdullah, S & Jaddi, NS 2010, Great deluge algorithm for rough set attribute reduction. in Communications in Computer and Information Science. vol. 118 CCIS, Communications in Computer and Information Science, vol. 118 CCIS, pp. 189-197, 2010 International Conferences on Database Theory and Application, DTA 2010 and Bio-Science and Bio-Technology, BSBT 2010, Held as Part of the 2nd International Mega-Conference on Future Generation Information Technology, FGIT 2010, Jeju Island, 13/12/10. https://doi.org/10.1007/978-3-642-17622-7_19
Abdullah S, Jaddi NS. Great deluge algorithm for rough set attribute reduction. In Communications in Computer and Information Science. Vol. 118 CCIS. 2010. p. 189-197. (Communications in Computer and Information Science). https://doi.org/10.1007/978-3-642-17622-7_19
Abdullah, Salwani ; Jaddi, Najmeh Sadat. / Great deluge algorithm for rough set attribute reduction. Communications in Computer and Information Science. Vol. 118 CCIS 2010. pp. 189-197 (Communications in Computer and Information Science).
@inproceedings{fa011636d6394b6aaad4ccab737d7b3c,
title = "Great deluge algorithm for rough set attribute reduction",
abstract = "Attribute reduction is the process of selecting a subset of features from the original set of features that forms patterns in a given dataset. It can be defined as a process to eliminate redundant attributes and at the same time is able to avoid any information loss, so that the selected subset is sufficient to describe the original features. In this paper, we present a great deluge algorithm for attribute reduction in rough set theory (GD-RSAR). Great deluge is a meta-heuristic approach that is less parameter dependent. There are only two parameters needed; the time to {"}spend{"} and the expected final solution. The algorithm always accepts improved solutions. The worse solution will be accepted if it is better than the upper boundary value or {"}level{"}. GD-RSAR has been tested on the public domain datasets available in UCI. Experimental results on benchmark datasets demonstrate that this approach is effective and able to obtain competitive results compared to previous available methods. Possible extensions upon this simple approach are also discussed.",
keywords = "Attribute Reduction, Great Deluge, Rough Set",
author = "Salwani Abdullah and Jaddi, {Najmeh Sadat}",
year = "2010",
doi = "10.1007/978-3-642-17622-7_19",
language = "English",
isbn = "3642176216",
volume = "118 CCIS",
series = "Communications in Computer and Information Science",
pages = "189--197",
booktitle = "Communications in Computer and Information Science",

}

TY - GEN

T1 - Great deluge algorithm for rough set attribute reduction

AU - Abdullah, Salwani

AU - Jaddi, Najmeh Sadat

PY - 2010

Y1 - 2010

N2 - Attribute reduction is the process of selecting a subset of features from the original set of features that forms patterns in a given dataset. It can be defined as a process to eliminate redundant attributes and at the same time is able to avoid any information loss, so that the selected subset is sufficient to describe the original features. In this paper, we present a great deluge algorithm for attribute reduction in rough set theory (GD-RSAR). Great deluge is a meta-heuristic approach that is less parameter dependent. There are only two parameters needed; the time to "spend" and the expected final solution. The algorithm always accepts improved solutions. The worse solution will be accepted if it is better than the upper boundary value or "level". GD-RSAR has been tested on the public domain datasets available in UCI. Experimental results on benchmark datasets demonstrate that this approach is effective and able to obtain competitive results compared to previous available methods. Possible extensions upon this simple approach are also discussed.

AB - Attribute reduction is the process of selecting a subset of features from the original set of features that forms patterns in a given dataset. It can be defined as a process to eliminate redundant attributes and at the same time is able to avoid any information loss, so that the selected subset is sufficient to describe the original features. In this paper, we present a great deluge algorithm for attribute reduction in rough set theory (GD-RSAR). Great deluge is a meta-heuristic approach that is less parameter dependent. There are only two parameters needed; the time to "spend" and the expected final solution. The algorithm always accepts improved solutions. The worse solution will be accepted if it is better than the upper boundary value or "level". GD-RSAR has been tested on the public domain datasets available in UCI. Experimental results on benchmark datasets demonstrate that this approach is effective and able to obtain competitive results compared to previous available methods. Possible extensions upon this simple approach are also discussed.

KW - Attribute Reduction

KW - Great Deluge

KW - Rough Set

UR - http://www.scopus.com/inward/record.url?scp=78650772165&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78650772165&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-17622-7_19

DO - 10.1007/978-3-642-17622-7_19

M3 - Conference contribution

AN - SCOPUS:78650772165

SN - 3642176216

SN - 9783642176210

VL - 118 CCIS

T3 - Communications in Computer and Information Science

SP - 189

EP - 197

BT - Communications in Computer and Information Science

ER -