Consistency based feature selection

Manoranjan Dash; Huan Liu; Hiroshi Motoda

doi:10.1007/3-540-45571-x_12

Consistency based feature selection

Manoranjan Dash, Huan Liu, Hiroshi Motoda

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

137 Scopus citations

Abstract

Feature selection is an effective technique in dealing with dimensionality reduction for classification task, a main component of data mining. It searches for ein "optimal" subset of features. The search strategies under consideration are one of the three: complete, heuristic, and probabilistic. Existing algorithms adopt various measiires to evaluate the goodness of feature subsets. This work focuses on one measure called consistency. We study its properties in compsirison with other major measures and different ways of using this measure in sejirch of feature subsets. We conduct cin empirical study to examine the pros and cons of these different search methods using consistency. Through this extensive exercise, we ciim to provide a comprehensive view of this measure and its relations with other measures cind a guideline of the use of this meeisure with different search strategies facing a new application.

Original language	English (US)
Title of host publication	Knowledge Discovery and Data Mining
Subtitle of host publication	Current Issues and New Applications - 4th Pacific-Asia Conference, PAKDD 2000, Proceedings
Editors	Takao Terano, Huan Liu, Arbee L.P. Chen
Publisher	Springer Verlag
Pages	98-109
Number of pages	12
ISBN (Print)	3540673822, 9783540673828
DOIs	https://doi.org/10.1007/3-540-45571-x_12
State	Published - 2000
Externally published	Yes
Event	4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2000 - Kyoto, Japan Duration: Apr 18 2000 → Apr 20 2000

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	1805
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2000
Country/Territory	Japan
City	Kyoto
Period	4/18/00 → 4/20/00

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/3-540-45571-x_12

Cite this

Dash, M., Liu, H., & Motoda, H. (2000). Consistency based feature selection. In T. Terano, H. Liu, & A. L. P. Chen (Eds.), Knowledge Discovery and Data Mining: Current Issues and New Applications - 4th Pacific-Asia Conference, PAKDD 2000, Proceedings (pp. 98-109). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1805). Springer Verlag. https://doi.org/10.1007/3-540-45571-x_12

Consistency based feature selection. / Dash, Manoranjan; Liu, Huan; Motoda, Hiroshi.
Knowledge Discovery and Data Mining: Current Issues and New Applications - 4th Pacific-Asia Conference, PAKDD 2000, Proceedings. ed. / Takao Terano; Huan Liu; Arbee L.P. Chen. Springer Verlag, 2000. p. 98-109 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1805).

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

Dash, M, Liu, H & Motoda, H 2000, Consistency based feature selection. in T Terano, H Liu & ALP Chen (eds), Knowledge Discovery and Data Mining: Current Issues and New Applications - 4th Pacific-Asia Conference, PAKDD 2000, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1805, Springer Verlag, pp. 98-109, 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2000, Kyoto, Japan, 4/18/00. https://doi.org/10.1007/3-540-45571-x_12

Dash M, Liu H, Motoda H. Consistency based feature selection. In Terano T, Liu H, Chen ALP, editors, Knowledge Discovery and Data Mining: Current Issues and New Applications - 4th Pacific-Asia Conference, PAKDD 2000, Proceedings. Springer Verlag. 2000. p. 98-109. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-45571-x_12

Dash, Manoranjan ; Liu, Huan ; Motoda, Hiroshi. / Consistency based feature selection. Knowledge Discovery and Data Mining: Current Issues and New Applications - 4th Pacific-Asia Conference, PAKDD 2000, Proceedings. editor / Takao Terano ; Huan Liu ; Arbee L.P. Chen. Springer Verlag, 2000. pp. 98-109 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{1bdde53d5d0a42e9aadd67bcf007def0,

title = "Consistency based feature selection",

abstract = "Feature selection is an effective technique in dealing with dimensionality reduction for classification task, a main component of data mining. It searches for ein {"}optimal{"} subset of features. The search strategies under consideration are one of the three: complete, heuristic, and probabilistic. Existing algorithms adopt various measiires to evaluate the goodness of feature subsets. This work focuses on one measure called consistency. We study its properties in compsirison with other major measures and different ways of using this measure in sejirch of feature subsets. We conduct cin empirical study to examine the pros and cons of these different search methods using consistency. Through this extensive exercise, we ciim to provide a comprehensive view of this measure and its relations with other measures cind a guideline of the use of this meeisure with different search strategies facing a new application.",

author = "Manoranjan Dash and Huan Liu and Hiroshi Motoda",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2000.; 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2000 ; Conference date: 18-04-2000 Through 20-04-2000",

year = "2000",

doi = "10.1007/3-540-45571-x_12",

language = "English (US)",

isbn = "3540673822",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "98--109",

editor = "Takao Terano and Huan Liu and Chen, {Arbee L.P.}",

booktitle = "Knowledge Discovery and Data Mining",

}

TY - GEN

T1 - Consistency based feature selection

AU - Dash, Manoranjan

AU - Liu, Huan

AU - Motoda, Hiroshi

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2000.

PY - 2000

Y1 - 2000

N2 - Feature selection is an effective technique in dealing with dimensionality reduction for classification task, a main component of data mining. It searches for ein "optimal" subset of features. The search strategies under consideration are one of the three: complete, heuristic, and probabilistic. Existing algorithms adopt various measiires to evaluate the goodness of feature subsets. This work focuses on one measure called consistency. We study its properties in compsirison with other major measures and different ways of using this measure in sejirch of feature subsets. We conduct cin empirical study to examine the pros and cons of these different search methods using consistency. Through this extensive exercise, we ciim to provide a comprehensive view of this measure and its relations with other measures cind a guideline of the use of this meeisure with different search strategies facing a new application.

AB - Feature selection is an effective technique in dealing with dimensionality reduction for classification task, a main component of data mining. It searches for ein "optimal" subset of features. The search strategies under consideration are one of the three: complete, heuristic, and probabilistic. Existing algorithms adopt various measiires to evaluate the goodness of feature subsets. This work focuses on one measure called consistency. We study its properties in compsirison with other major measures and different ways of using this measure in sejirch of feature subsets. We conduct cin empirical study to examine the pros and cons of these different search methods using consistency. Through this extensive exercise, we ciim to provide a comprehensive view of this measure and its relations with other measures cind a guideline of the use of this meeisure with different search strategies facing a new application.

UR - http://www.scopus.com/inward/record.url?scp=84942785917&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84942785917&partnerID=8YFLogxK

U2 - 10.1007/3-540-45571-x_12

DO - 10.1007/3-540-45571-x_12

M3 - Conference contribution

AN - SCOPUS:84942785917

SN - 3540673822

SN - 9783540673828

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 98

EP - 109

BT - Knowledge Discovery and Data Mining

A2 - Terano, Takao

A2 - Liu, Huan

A2 - Chen, Arbee L.P.

PB - Springer Verlag

T2 - 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2000

Y2 - 18 April 2000 through 20 April 2000

ER -

Consistency based feature selection

Abstract

Publication series

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this