Unsupervised Feature Subset Selection

Nicolaj Søndberg-Madsen; C. Thomsen; Jose Pena

Unsupervised Feature Subset Selection

Nicolaj Søndberg-Madsen, C. Thomsen, Jose Pena

Institut for Datalogi

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning

Abstract

This paper studies filter and hybrid filter-wrapper feature subset selection for unsupervised learning (data clustering). We constrain the search for the best feature subset by scoring the dependence of every feature on the rest of the features, conjecturing that these scores discriminate some irrelevant features. We report experimental results on artificial and real data for unsupervised learning of naive Bayes models. Both the filter and hybrid approaches perform satisfactorily.

Originalsprog	Engelsk
Titel	Proceedings on the Workshop on Probabilistic Graphical Models for Classification : (within ECML/PKDD 2003)
Antal sider	11
Publikationsdato	2003
Sider	71-82
Status	Udgivet - 2003
Begivenhed	ECML/PKDD - Cavtat-Dubrovnik, Kroatien Varighed: 22 sep. 2003 → 26 sep. 2003 Konferencens nummer: 14th / 7th

Konference

Konference	ECML/PKDD
Nummer	14th / 7th
Land/Område	Kroatien
By	Cavtat-Dubrovnik
Periode	22/09/2003 → 26/09/2003

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Citationsformater

@inproceedings{f4fcb050a78911da881a000ea68e967b,

title = "Unsupervised Feature Subset Selection",

abstract = "This paper studies filter and hybrid filter-wrapper feature subset selection for unsupervised learning (data clustering). We constrain the search for the best feature subset by scoring the dependence of every feature on the rest of the features, conjecturing that these scores discriminate some irrelevant features. We report experimental results on artificial and real data for unsupervised learning of naive Bayes models. Both the filter and hybrid approaches perform satisfactorily.",

keywords = "feature selection, data-clustering, EM-algorithm, dependence measure",

author = "Nicolaj S{\o}ndberg-Madsen and C. Thomsen and Jose Pena",

year = "2003",

language = "English",

pages = "71--82",

booktitle = "Proceedings on the Workshop on Probabilistic Graphical Models for Classification",

note = "ECML/PKDD ; Conference date: 22-09-2003 Through 26-09-2003",

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T1 - Unsupervised Feature Subset Selection

AU - Søndberg-Madsen, Nicolaj

AU - Thomsen, C.

AU - Pena, Jose

N1 - Conference code: 14th / 7th

PY - 2003

Y1 - 2003

N2 - This paper studies filter and hybrid filter-wrapper feature subset selection for unsupervised learning (data clustering). We constrain the search for the best feature subset by scoring the dependence of every feature on the rest of the features, conjecturing that these scores discriminate some irrelevant features. We report experimental results on artificial and real data for unsupervised learning of naive Bayes models. Both the filter and hybrid approaches perform satisfactorily.

AB - This paper studies filter and hybrid filter-wrapper feature subset selection for unsupervised learning (data clustering). We constrain the search for the best feature subset by scoring the dependence of every feature on the rest of the features, conjecturing that these scores discriminate some irrelevant features. We report experimental results on artificial and real data for unsupervised learning of naive Bayes models. Both the filter and hybrid approaches perform satisfactorily.

KW - feature selection

KW - data-clustering

KW - EM-algorithm

KW - dependence measure

M3 - Article in proceeding

SP - 71

EP - 82

BT - Proceedings on the Workshop on Probabilistic Graphical Models for Classification

T2 - ECML/PKDD

Y2 - 22 September 2003 through 26 September 2003

ER -

Unsupervised Feature Subset Selection

Abstract

Konference

AUB Link

Fingeraftryk

Citationsformater