Large Margin Distribution Machine Recursive Feature Elimination

Ge Ou; Yan Wang; Wei Pang; George MacLeod Coghill

Large Margin Distribution Machine Recursive Feature Elimination

Ge Ou, Yan Wang, Wei Pang, George MacLeod Coghill

Jilin University

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

7 Citations (Scopus)

33 Downloads (Pure)

Abstract

—In order to eliminate irrelevant features for classification, we propose a novel feature selection algorithm called Large Margin Distribution Machine Recursive Feature Elimination (LDM-RFE). LDM-RFE uses the latest support vector based classification algorithm Large Margin Distribution Machine (LDM) to evaluate all the features of samples, and then generates a ranked feature list during the procedure of Recursive Feature Elimination (RFE). In the experiment section, we report
promising results obtained by LDM-RFE in comparison with several common feature selection algorithms on five UCI benchmark datasets.

Original language	English
Title of host publication	The 2017 4th International Conference on Systems and Informatics (ICSAI 2017)
Publisher	IEEE Press
Pages	1427-1432
Number of pages	6
ISBN (Print)	978-1-5386-1106-7
Publication status	Published - 2017
Event	The 2017 4th International Conference on Systems and Informatics - Hangzhou Zhejiang, China Duration: 11 Nov 2017 → 13 Nov 2017

Conference

Conference	The 2017 4th International Conference on Systems and Informatics
Abbreviated title	ICSAI 2017
Country/Territory	China
City	Hangzhou Zhejiang
Period	11/11/17 → 13/11/17

Bibliographical note

We gratefully thank Dr Teng Zhang and Prof Zhi-Hua
Zhou for providing the source code of “LDM” source code and
their kind technical assistance. This work is supported by the
National Natural Science Foundation of China (Nos.
61472159, 61572227) and Development Project of Jilin
Province of China (Nos. 20160204022GX, 2017C033). This
work is also partially supported by the 2015 Scottish Crucible
Award funded by the Royal Society of Edinburgh and the 2016
PECE bursary provided by the Scottish Informatics &
Computer Science Alliance (SICSA).

Keywords

large margin distribution machine
recursive feature elimination
classification

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LDMRFE_final20171022og_FinalAccepted
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Accepted author manuscript, 1.43 MBLicence: Other

Cite this

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title = "Large Margin Distribution Machine Recursive Feature Elimination",

abstract = "—In order to eliminate irrelevant features for classification, we propose a novel feature selection algorithm called Large Margin Distribution Machine Recursive Feature Elimination (LDM-RFE). LDM-RFE uses the latest support vector based classification algorithm Large Margin Distribution Machine (LDM) to evaluate all the features of samples, and then generates a ranked feature list during the procedure of Recursive Feature Elimination (RFE). In the experiment section, we reportpromising results obtained by LDM-RFE in comparison with several common feature selection algorithms on five UCI benchmark datasets. ",

keywords = "large margin distribution machine, recursive feature elimination, classification",

author = "Ge Ou and Yan Wang and Wei Pang and Coghill, {George MacLeod}",

note = "We gratefully thank Dr Teng Zhang and Prof Zhi-Hua Zhou for providing the source code of “LDM” source code and their kind technical assistance. This work is supported by the National Natural Science Foundation of China (Nos. 61472159, 61572227) and Development Project of Jilin Province of China (Nos. 20160204022GX, 2017C033). This work is also partially supported by the 2015 Scottish Crucible Award funded by the Royal Society of Edinburgh and the 2016 PECE bursary provided by the Scottish Informatics & Computer Science Alliance (SICSA). ; The 2017 4th International Conference on Systems and Informatics , ICSAI 2017 ; Conference date: 11-11-2017 Through 13-11-2017",

year = "2017",

language = "English",

isbn = "978-1-5386-1106-7",

pages = "1427--1432",

booktitle = "The 2017 4th International Conference on Systems and Informatics (ICSAI 2017)",

publisher = "IEEE Press",

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TY - GEN

T1 - Large Margin Distribution Machine Recursive Feature Elimination

AU - Ou, Ge

AU - Wang, Yan

AU - Pang, Wei

AU - Coghill, George MacLeod

N1 - We gratefully thank Dr Teng Zhang and Prof Zhi-Hua Zhou for providing the source code of “LDM” source code and their kind technical assistance. This work is supported by the National Natural Science Foundation of China (Nos. 61472159, 61572227) and Development Project of Jilin Province of China (Nos. 20160204022GX, 2017C033). This work is also partially supported by the 2015 Scottish Crucible Award funded by the Royal Society of Edinburgh and the 2016 PECE bursary provided by the Scottish Informatics & Computer Science Alliance (SICSA).

PY - 2017

Y1 - 2017

N2 - —In order to eliminate irrelevant features for classification, we propose a novel feature selection algorithm called Large Margin Distribution Machine Recursive Feature Elimination (LDM-RFE). LDM-RFE uses the latest support vector based classification algorithm Large Margin Distribution Machine (LDM) to evaluate all the features of samples, and then generates a ranked feature list during the procedure of Recursive Feature Elimination (RFE). In the experiment section, we reportpromising results obtained by LDM-RFE in comparison with several common feature selection algorithms on five UCI benchmark datasets.

AB - —In order to eliminate irrelevant features for classification, we propose a novel feature selection algorithm called Large Margin Distribution Machine Recursive Feature Elimination (LDM-RFE). LDM-RFE uses the latest support vector based classification algorithm Large Margin Distribution Machine (LDM) to evaluate all the features of samples, and then generates a ranked feature list during the procedure of Recursive Feature Elimination (RFE). In the experiment section, we reportpromising results obtained by LDM-RFE in comparison with several common feature selection algorithms on five UCI benchmark datasets.

KW - large margin distribution machine

KW - recursive feature elimination

KW - classification

M3 - Published conference contribution

SN - 978-1-5386-1106-7

SP - 1427

EP - 1432

BT - The 2017 4th International Conference on Systems and Informatics (ICSAI 2017)

PB - IEEE Press

T2 - The 2017 4th International Conference on Systems and Informatics

Y2 - 11 November 2017 through 13 November 2017

ER -

Large Margin Distribution Machine Recursive Feature Elimination

Abstract

Conference

Bibliographical note

Keywords

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