ε-Distance Weighted Support Vector Regression

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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 languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part I
EditorsDinh Phung, Vincent S. Tseng, Prof. Geoffrey I. Webb, Bao Ho, Mohadeseh Ganji, Lida Rashidi
PublisherSpringer International Publishing
ISBN (Electronic)9783319930343
ISBN (Print)9783319930336
DOIs
Publication statusPublished - 22 Jul 2018
EventPAKDD 2018: The 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining - Melbourne, Australia
Duration: 3 Jun 20186 Jun 2018
http://prada-research.net/pakdd18/

Publication series

NameLecture Notes in Artificial Intelligence
PublisherSpringer
ISSN (Print)0302-9743

Conference

ConferencePAKDD 2018
Abbreviated titlePAKDD 2018
CountryAustralia
CityMelbourne
Period3/06/186/06/18
Internet address

Keywords

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

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 [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; Pang, Wei; Coghill, George MacLeod.

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 proceedingConference 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). https://doi.org/10.1007/978-3-319-93034-3_17
Ou, Ge ; Wang, Yan ; Huang, Lan ; Pang, Wei ; Coghill, George MacLeod. / 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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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).",
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N1 - 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).

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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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