The Support Feature Machine for Classifying with the Least Number of Features

Sascha Klement; Thomas Martinetz

doi:10.1007/978-3-642-15822-3_11

The Support Feature Machine for Classifying with the Least Number of Features

Institut für Neuro- und Bioinformatik

Abstract

We propose the so-called Support Feature Machine (SFM) as a novel approach to feature selection for classification, based on minimisation of the zero norm of a separating hyperplane. Thus, a classifier with inherent feature selection capabilities is obtained within a single training run. Results on toy examples demonstrate that this method is able to identify relevant features very effectively.

Originalsprache	Englisch
Titel	Artificial Neural Networks -- ICANN 2010
Redakteure/-innen	Konstantinos Diamantaras, Wlodek Duch, Lazaros S. Iliadis
Seitenumfang	6
Band	6353
Erscheinungsort	Berlin, Heidelberg
Herausgeber (Verlag)	Springer Berlin Heidelberg
Erscheinungsdatum	2010
Seiten	88-93
ISBN (Print)	978-3-642-15821-6
ISBN (elektronisch)	978-3-642-15822-3
DOIs	https://doi.org/10.1007/978-3-642-15822-3_11
Publikationsstatus	Veröffentlicht - 2010
Veranstaltung	20th International Conference Artificial Neural Networks - Thessaloniki, Griechenland Dauer: 15.09.2010 → 18.09.2010

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10.1007/978-3-642-15822-3_11

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@inproceedings{1380ae9541d949bab3f536e133198730,

title = "The Support Feature Machine for Classifying with the Least Number of Features",

abstract = "We propose the so-called Support Feature Machine (SFM) as a novel approach to feature selection for classification, based on minimisation of the zero norm of a separating hyperplane. Thus, a classifier with inherent feature selection capabilities is obtained within a single training run. Results on toy examples demonstrate that this method is able to identify relevant features very effectively.",

author = "Sascha Klement and Thomas Martinetz",

year = "2010",

doi = "10.1007/978-3-642-15822-3_11",

language = "English",

isbn = "978-3-642-15821-6",

volume = "6353",

series = "Lecture Notes in Computer Science",

publisher = "Springer Berlin Heidelberg",

pages = "88--93",

editor = "Konstantinos Diamantaras and Wlodek Duch and Iliadis, {Lazaros S.}",

booktitle = "Artificial Neural Networks -- ICANN 2010",

note = "20th International Conference Artificial Neural Networks<br/>, ICANN 2010 ; Conference date: 15-09-2010 Through 18-09-2010",

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Klement, S & Martinetz, T 2010, The Support Feature Machine for Classifying with the Least Number of Features. in K Diamantaras, W Duch & LS Iliadis (Hrsg.), Artificial Neural Networks -- ICANN 2010. Bd. 6353, Lecture Notes in Computer Science, Bd. 6353, Springer Berlin Heidelberg, Berlin, Heidelberg, S. 88-93, 20th International Conference Artificial Neural Networks
, Thessaloniki, Griechenland, 15.09.10. https://doi.org/10.1007/978-3-642-15822-3_11

The Support Feature Machine for Classifying with the Least Number of Features. / Klement, Sascha; Martinetz, Thomas.
Artificial Neural Networks -- ICANN 2010. Hrsg. / Konstantinos Diamantaras; Wlodek Duch; Lazaros S. Iliadis. Band 6353 Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. S. 88-93 (Lecture Notes in Computer Science; Band 6353).

Publikation: Kapitel in Büchern/Berichten/Konferenzbänden › Konferenzbeitrag › Begutachtung

TY - GEN

T1 - The Support Feature Machine for Classifying with the Least Number of Features

AU - Klement, Sascha

AU - Martinetz, Thomas

PY - 2010

Y1 - 2010

N2 - We propose the so-called Support Feature Machine (SFM) as a novel approach to feature selection for classification, based on minimisation of the zero norm of a separating hyperplane. Thus, a classifier with inherent feature selection capabilities is obtained within a single training run. Results on toy examples demonstrate that this method is able to identify relevant features very effectively.

AB - We propose the so-called Support Feature Machine (SFM) as a novel approach to feature selection for classification, based on minimisation of the zero norm of a separating hyperplane. Thus, a classifier with inherent feature selection capabilities is obtained within a single training run. Results on toy examples demonstrate that this method is able to identify relevant features very effectively.

U2 - 10.1007/978-3-642-15822-3_11

DO - 10.1007/978-3-642-15822-3_11

M3 - Conference contribution

SN - 978-3-642-15821-6

VL - 6353

T3 - Lecture Notes in Computer Science

SP - 88

EP - 93

BT - Artificial Neural Networks -- ICANN 2010

A2 - Diamantaras, Konstantinos

A2 - Duch, Wlodek

A2 - Iliadis, Lazaros S.

PB - Springer Berlin Heidelberg

CY - Berlin, Heidelberg

T2 - 20th International Conference Artificial Neural Networks<br/>

Y2 - 15 September 2010 through 18 September 2010

ER -

The Support Feature Machine for Classifying with the Least Number of Features

Abstract

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