Hierarchical feature selection for biological data

Witold Jacak; Karin Pröll

Hierarchical feature selection for biological data

Witold Jacak
, Karin Pröll

Research Center Hagenberg

Publikation: Beitrag in Buch/Bericht/Tagungsband › Konferenzbeitrag › Begutachtung

Abstract

In this paper we present feature selection in biological data by combining unsupervised learning with supervised cross validation. Unsupervised clustering methods are used to perform a clustering of object-data for a chosen subset of input features and given number of clusters. The resulting object clusters are compared with the predefined original object classes and a matching factor (score) is calculated. This score is used as criterion function for heuristic sequential feature selection and a cross selection algorithm.

Originalsprache	Englisch
Titel	26th European Modeling and Simulation Symposium, EMSS 2014
Redakteure/-innen	Yuri Merkuryev, Lin Zhang, Emilio Jimenez, Francesco Longo, Michael Affenzeller, Agostino G. Bruzzone
Herausgeber (Verlag)	DIPTEM University of Genova
Seiten	93-97
Seitenumfang	5
ISBN (elektronisch)	9788897999324
ISBN (Print)	978-88-97999-38-6
Publikationsstatus	Veröffentlicht - 2014
Veranstaltung	The 26th European Modeling & Simulation Symposium EMSS 2014 - Bordeaux, Frankreich Dauer: 10 Sep. 2014 → 12 Sep. 2014 http://www.msc-les.org/conf/emss2014/index.htm

Publikationsreihe

Name	26th European Modeling and Simulation Symposium, EMSS 2014

Konferenz

Konferenz	The 26th European Modeling & Simulation Symposium EMSS 2014
Land/Gebiet	Frankreich
Ort	Bordeaux
Zeitraum	10.09.2014 → 12.09.2014
Internetadresse	http://www.msc-les.org/conf/emss2014/index.htm

Schlagwörter

classification
clustering
feature selection
sequential feature selection

Andere Dateien und Links

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@inproceedings{9bc3a91f9ea342268f89d8c09b869401,

title = "Hierarchical feature selection for biological data",

abstract = "In this paper we present feature selection in biological data by combining unsupervised learning with supervised cross validation. Unsupervised clustering methods are used to perform a clustering of object-data for a chosen subset of input features and given number of clusters. The resulting object clusters are compared with the predefined original object classes and a matching factor (score) is calculated. This score is used as criterion function for heuristic sequential feature selection and a cross selection algorithm.",

keywords = "classification, clustering, feature selection, sequential feature selection, classification, clustering, feature selection, sequential feature selection, Sequential feature selection, Feature selection, Classification, Clustering",

author = "Witold Jacak and Karin Pr{\"o}ll",

year = "2014",

language = "English",

isbn = "978-88-97999-38-6",

series = "26th European Modeling and Simulation Symposium, EMSS 2014",

publisher = "DIPTEM University of Genova",

pages = "93--97",

editor = "Yuri Merkuryev and Lin Zhang and Emilio Jimenez and Francesco Longo and Michael Affenzeller and Bruzzone, \{Agostino G.\}",

booktitle = "26th European Modeling and Simulation Symposium, EMSS 2014",

note = "The 26th European Modeling \& Simulation Symposium EMSS 2014 ; Conference date: 10-09-2014 Through 12-09-2014",

url = "http://www.msc-les.org/conf/emss2014/index.htm",

}

Jacak, W & Pröll, K 2014, Hierarchical feature selection for biological data. in Y Merkuryev, L Zhang, E Jimenez, F Longo, M Affenzeller & AG Bruzzone (Hrsg.), 26th European Modeling and Simulation Symposium, EMSS 2014. 26th European Modeling and Simulation Symposium, EMSS 2014, DIPTEM University of Genova, S. 93-97, The 26th European Modeling & Simulation Symposium EMSS 2014, Bordeaux, Frankreich, 10.09.2014.

Hierarchical feature selection for biological data. / Jacak, Witold; Pröll, Karin.
26th European Modeling and Simulation Symposium, EMSS 2014. Hrsg. / Yuri Merkuryev; Lin Zhang; Emilio Jimenez; Francesco Longo; Michael Affenzeller; Agostino G. Bruzzone. DIPTEM University of Genova, 2014. S. 93-97 (26th European Modeling and Simulation Symposium, EMSS 2014).

Publikation: Beitrag in Buch/Bericht/Tagungsband › Konferenzbeitrag › Begutachtung

TY - GEN

T1 - Hierarchical feature selection for biological data

AU - Jacak, Witold

AU - Pröll, Karin

PY - 2014

Y1 - 2014

N2 - In this paper we present feature selection in biological data by combining unsupervised learning with supervised cross validation. Unsupervised clustering methods are used to perform a clustering of object-data for a chosen subset of input features and given number of clusters. The resulting object clusters are compared with the predefined original object classes and a matching factor (score) is calculated. This score is used as criterion function for heuristic sequential feature selection and a cross selection algorithm.

AB - In this paper we present feature selection in biological data by combining unsupervised learning with supervised cross validation. Unsupervised clustering methods are used to perform a clustering of object-data for a chosen subset of input features and given number of clusters. The resulting object clusters are compared with the predefined original object classes and a matching factor (score) is calculated. This score is used as criterion function for heuristic sequential feature selection and a cross selection algorithm.

KW - classification

KW - clustering

KW - feature selection

KW - sequential feature selection

KW - classification

KW - clustering

KW - feature selection

KW - sequential feature selection

KW - Sequential feature selection

KW - Feature selection

KW - Classification

KW - Clustering

UR - https://www.scopus.com/pages/publications/84912099477

M3 - Conference contribution

SN - 978-88-97999-38-6

T3 - 26th European Modeling and Simulation Symposium, EMSS 2014

SP - 93

EP - 97

BT - 26th European Modeling and Simulation Symposium, EMSS 2014

A2 - Merkuryev, Yuri

A2 - Zhang, Lin

A2 - Jimenez, Emilio

A2 - Longo, Francesco

A2 - Affenzeller, Michael

A2 - Bruzzone, Agostino G.

PB - DIPTEM University of Genova

T2 - The 26th European Modeling & Simulation Symposium EMSS 2014

Y2 - 10 September 2014 through 12 September 2014

ER -

Hierarchical feature selection for biological data

Abstract

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Konferenz

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