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 publicationPAKDD 2018: Advances in Knowledge Discovery and Data Mining
EditorsDinh Phung, Vincent S. Tseng, Geoffrey I. Webb, Bao Ho, Mohadeseh Ganji, Lida Rashidi
PublisherSpringer
Pages209-220
ISBN (Electronic)978-3-319-93034-3
ISBN (Print)9783319930336
DOIs
Publication statusPublished - 2018
Event22nd Pacific-Asia Conference 2018 - Melbourne, Australia
Duration: 3 Jun 20186 Jun 2018

Publication series

Name Lecture Notes in Artificial Intelligence
Volume10937

Conference

Conference22nd Pacific-Asia Conference 2018
Abbreviated titlePAKDD 2018
CountryAustralia
CityMelbourne
Period3/06/186/06/18

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, G. I. Webb, B. Ho, M. Ganji, & L. Rashidi (Eds.), PAKDD 2018: Advances in Knowledge Discovery and Data Mining (pp. 209-220). ( Lecture Notes in Artificial Intelligence ; Vol. 10937). Springer. https://doi.org/10.1007/978-3-319-93034-3_17