Ridge-adjusted Slack Variable Optimization for supervised classification

Yinan Yu, Konstantinos I. Diamantaras, Tomas McKelvey, S. Y. Kung

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

4 Scopus citations

Abstract

This paper presents an iterative classification algorithm called Ridge-adjusted Slack Variable Optimization (RiSVO). RiSVO is an iterative procedure with two steps: (1) A working subset of the training data is selected so as to reject 'extreme' patterns. (2) the decision vector and threshold value are obtained by minimizing the energy function associated with the slack variables. From a computational perspective, we have established a sufficient condition for the 'inclusion property' among successive working sets, which allows us to save computation time. Most importantly, under the inclusion property, the monotonic reduction of the energy function can be assured in both substeps at each iteration, thus assuring the convergence of the algorithm. Moreover, ridge regularization is incorporated to improve the robustness and better cope with over-fitting and ill-conditioned problems. To verify the proposed algorithm, we conducted simulations on three data sets from the UCI database: adult, shuttle and bank. Our simulation shows stability and convergence of the RiSVO method. The results also show improvement of performance over the SVM classifier.

Original languageEnglish (US)
Title of host publication2013 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2013
DOIs
StatePublished - Dec 1 2013
Event2013 16th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2013 - Southampton, United Kingdom
Duration: Sep 22 2013Sep 25 2013

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Other

Other2013 16th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2013
CountryUnited Kingdom
CitySouthampton
Period9/22/139/25/13

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Signal Processing

Keywords

  • classification
  • kernel method
  • ridge-regression
  • slack energy minimization
  • training data selection

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  • Cite this

    Yu, Y., Diamantaras, K. I., McKelvey, T., & Kung, S. Y. (2013). Ridge-adjusted Slack Variable Optimization for supervised classification. In 2013 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2013 [6661982] (IEEE International Workshop on Machine Learning for Signal Processing, MLSP). https://doi.org/10.1109/MLSP.2013.6661982