Large Margin Distribution Machine Recursive Feature Elimination

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

doi:10.1109/ICSAI.2017.8248525

Large Margin Distribution Machine Recursive Feature Elimination

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

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

8 Citations (Scopus)

Abstract

In order to eliminate irrelevant features forclassification, we propose a novel feature selection algorithmcalled Large Margin Distribution Machine Recursive FeatureElimination (LDM-RFE). LDM-RFE uses the latest supportvector based classification algorithm Large Margin DistributionMachine (LDM) to evaluate all the features of samples, and thengenerates a ranked feature list during the procedure of RecursiveFeature Elimination (RFE). In the experiment section, we reportpromising results obtained by LDM-RFE in comparison withseveral common feature selection algorithms on five UCIbenchmark datasets.

Original language	English
Title of host publication	2017 4th International Conference on Systems and Informatics (ICSAI)
Publisher	IEEE
Pages	1518-1523
Number of pages	6
ISBN (Electronic)	9781538611074
DOIs	https://doi.org/10.1109/ICSAI.2017.8248525
Publication status	Published - 8 Jan 2018

Keywords

large margin distribution machine
recursive feature elimination
classification

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10.1109/ICSAI.2017.8248525

Cite this

@inproceedings{0ed0b54e965d4842990bdf9ce176075b,

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

abstract = "In order to eliminate irrelevant features forclassification, we propose a novel feature selection algorithmcalled Large Margin Distribution Machine Recursive FeatureElimination (LDM-RFE). LDM-RFE uses the latest supportvector based classification algorithm Large Margin DistributionMachine (LDM) to evaluate all the features of samples, and thengenerates a ranked feature list during the procedure of RecursiveFeature Elimination (RFE). In the experiment section, we reportpromising results obtained by LDM-RFE in comparison withseveral common feature selection algorithms on five UCIbenchmark 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).",

year = "2018",

month = jan,

day = "8",

doi = "10.1109/ICSAI.2017.8248525",

language = "English",

pages = "1518--1523",

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

publisher = "IEEE",

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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 - 2018/1/8

Y1 - 2018/1/8

N2 - In order to eliminate irrelevant features forclassification, we propose a novel feature selection algorithmcalled Large Margin Distribution Machine Recursive FeatureElimination (LDM-RFE). LDM-RFE uses the latest supportvector based classification algorithm Large Margin DistributionMachine (LDM) to evaluate all the features of samples, and thengenerates a ranked feature list during the procedure of RecursiveFeature Elimination (RFE). In the experiment section, we reportpromising results obtained by LDM-RFE in comparison withseveral common feature selection algorithms on five UCIbenchmark datasets.

AB - In order to eliminate irrelevant features forclassification, we propose a novel feature selection algorithmcalled Large Margin Distribution Machine Recursive FeatureElimination (LDM-RFE). LDM-RFE uses the latest supportvector based classification algorithm Large Margin DistributionMachine (LDM) to evaluate all the features of samples, and thengenerates a ranked feature list during the procedure of RecursiveFeature Elimination (RFE). In the experiment section, we reportpromising results obtained by LDM-RFE in comparison withseveral common feature selection algorithms on five UCIbenchmark datasets.

KW - large margin distribution machine

KW - recursive feature elimination

KW - classification

U2 - 10.1109/ICSAI.2017.8248525

DO - 10.1109/ICSAI.2017.8248525

M3 - Conference contribution

SP - 1518

EP - 1523

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

PB - IEEE

ER -

Large Margin Distribution Machine Recursive Feature Elimination

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Keywords

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