ε-Distance Weighted Support Vector Regression

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

doi:10.1007/978-3-319-93034-3_17

ε-Distance Weighted Support Vector Regression

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

Jilin University

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

2 Citations (Scopus)

8 Downloads (Pure)

Abstract

We propose a novel support vector regression approach called ε-Distance Weighted Support Vector Regression (ε-DWSVR). ε-DWSVR specifically addresses a challenging issue in support vector regression: how to deal with the situation when the distribution of the internal data in the ε-tube is different from that of the boundary data containing support vectors. The proposed ε-DWSVR optimizes the minimum margin and the mean of functional margin simultaneously to tackle this issue. To solve the new optimization problem arising from ε-DWSVR, we adoptdual coordinate descent (DCD) with kernel functions for medium-scale problems and also employ averaged stochastic gradient descent (ASGD) to make ε-DWSVR scalable to larger problems. We report promising results obtained by ε-DWSVR in comparison with five popular regression methods on sixteen UCI benchmark datasets.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining
Subtitle of host publication	22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part I
Editors	Dinh Phung, Vincent S. Tseng, Prof. Geoffrey I. Webb, Bao Ho, Mohadeseh Ganji, Lida Rashidi
Publisher	Springer International Publishing
ISBN (Electronic)	9783319930343
ISBN (Print)	9783319930336
DOIs	https://doi.org/10.1007/978-3-319-93034-3_17
Publication status	Published - 22 Jul 2018
Event	PAKDD 2018: The 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining - Melbourne, Australia Duration: 3 Jun 2018 → 6 Jun 2018 http://prada-research.net/pakdd18/

Publication series

Name	Lecture Notes in Artificial Intelligence
Publisher	Springer
ISSN (Print)	0302-9743

Conference

Conference	PAKDD 2018
Abbreviated title	PAKDD 2018
Country/Territory	Australia
City	Melbourne
Period	3/06/18 → 6/06/18
Internet address	http://prada-research.net/pakdd18/

Bibliographical note

We gratefully thank Dr Teng Zhang and Prof Zhi-Hua Zhou for providing the
source code of “LDM”, and their kind technical assistance. We also thank Prof
Chih-Jen Lins team for providing the LIBSVM and LIBLINEAR packages and
their support. This work is supported by the National Natural Science Foundation
of China (Grant Nos.61472159, 61572227) and Development Project of Jilin
Province of China (Grant Nos. 20140101180JC, 20160204022GX, 20180414012G
H). 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

regression analysis
Support Vector Regression
Distance Weighted Support Vector Regression
Dual Coordinate Descent
Averaged Stochastic Gradient Descent

Access to Document

10.1007/978-3-319-93034-3_17Licence: Unspecified

Accepted Manuscript
The final authenticated version is available online at https://doi.org/10.1007/978-3-319-93034-3_17
Accepted author manuscript, 558 KBLicence: Other

Cite this

Ou, G., Wang, Y., Huang, L., Pang, W., & Coghill, G. M. (2018). ε-Distance Weighted Support Vector Regression. In D. Phung, V. S. Tseng, P. G. I. Webb, B. Ho, M. Ganji, & L. Rashidi (Eds.), Advances in Knowledge Discovery and Data Mining: 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part I Article 17 (Lecture Notes in Artificial Intelligence). Springer International Publishing. https://doi.org/10.1007/978-3-319-93034-3_17

ε-Distance Weighted Support Vector Regression. / Ou, Ge; Wang, Yan; Huang, Lan et al.
Advances in Knowledge Discovery and Data Mining: 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part I. ed. / Dinh Phung; Vincent S. Tseng; Prof. Geoffrey I. Webb; Bao Ho; Mohadeseh Ganji; Lida Rashidi. Springer International Publishing, 2018. 17 (Lecture Notes in Artificial Intelligence).

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

Ou, G, Wang, Y, Huang, L, Pang, W & Coghill, GM 2018, ε-Distance Weighted Support Vector Regression. in D Phung, VS Tseng, PGI Webb, B Ho, M Ganji & L Rashidi (eds), Advances in Knowledge Discovery and Data Mining: 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part I., 17, Lecture Notes in Artificial Intelligence, Springer International Publishing, PAKDD 2018, Melbourne, Australia, 3/06/18. https://doi.org/10.1007/978-3-319-93034-3_17

Ou G, Wang Y, Huang L, Pang W, Coghill GM. ε-Distance Weighted Support Vector Regression. In Phung D, Tseng VS, Webb PGI, Ho B, Ganji M, Rashidi L, editors, Advances in Knowledge Discovery and Data Mining: 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part I. Springer International Publishing. 2018. 17. (Lecture Notes in Artificial Intelligence). doi: 10.1007/978-3-319-93034-3_17

Ou, Ge ; Wang, Yan ; Huang, Lan et al. / ε-Distance Weighted Support Vector Regression. Advances in Knowledge Discovery and Data Mining: 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part I. editor / Dinh Phung ; Vincent S. Tseng ; Prof. Geoffrey I. Webb ; Bao Ho ; Mohadeseh Ganji ; Lida Rashidi. Springer International Publishing, 2018. (Lecture Notes in Artificial Intelligence).

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abstract = "We propose a novel support vector regression approach called ε-Distance Weighted Support Vector Regression (ε-DWSVR). ε-DWSVR specifically addresses a challenging issue in support vector regression: how to deal with the situation when the distribution of the internal data in the ε-tube is different from that of the boundary data containing support vectors. The proposed ε-DWSVR optimizes the minimum margin and the mean of functional margin simultaneously to tackle this issue. To solve the new optimization problem arising from ε-DWSVR, we adoptdual coordinate descent (DCD) with kernel functions for medium-scale problems and also employ averaged stochastic gradient descent (ASGD) to make ε-DWSVR scalable to larger problems. We report promising results obtained by ε-DWSVR in comparison with five popular regression methods on sixteen UCI benchmark datasets.",

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author = "Ge Ou and Yan Wang and Lan Huang 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”, and their kind technical assistance. We also thank Prof Chih-Jen Lins team for providing the LIBSVM and LIBLINEAR packages and their support. This work is supported by the National Natural Science Foundation of China (Grant Nos.61472159, 61572227) and Development Project of Jilin Province of China (Grant Nos. 20140101180JC, 20160204022GX, 20180414012G H). 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).; PAKDD 2018 : The 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018 ; Conference date: 03-06-2018 Through 06-06-2018",

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N2 - We propose a novel support vector regression approach called ε-Distance Weighted Support Vector Regression (ε-DWSVR). ε-DWSVR specifically addresses a challenging issue in support vector regression: how to deal with the situation when the distribution of the internal data in the ε-tube is different from that of the boundary data containing support vectors. The proposed ε-DWSVR optimizes the minimum margin and the mean of functional margin simultaneously to tackle this issue. To solve the new optimization problem arising from ε-DWSVR, we adoptdual coordinate descent (DCD) with kernel functions for medium-scale problems and also employ averaged stochastic gradient descent (ASGD) to make ε-DWSVR scalable to larger problems. We report promising results obtained by ε-DWSVR in comparison with five popular regression methods on sixteen UCI benchmark datasets.

AB - We propose a novel support vector regression approach called ε-Distance Weighted Support Vector Regression (ε-DWSVR). ε-DWSVR specifically addresses a challenging issue in support vector regression: how to deal with the situation when the distribution of the internal data in the ε-tube is different from that of the boundary data containing support vectors. The proposed ε-DWSVR optimizes the minimum margin and the mean of functional margin simultaneously to tackle this issue. To solve the new optimization problem arising from ε-DWSVR, we adoptdual coordinate descent (DCD) with kernel functions for medium-scale problems and also employ averaged stochastic gradient descent (ASGD) to make ε-DWSVR scalable to larger problems. We report promising results obtained by ε-DWSVR in comparison with five popular regression methods on sixteen UCI benchmark datasets.

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ER -

ε-Distance Weighted Support Vector Regression

Abstract

Publication series

Conference

Bibliographical note

Keywords

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