Uncertainty support vector method for ordinal regression

  • Authors:

  • Liu Guangli;Sun Ruizhi;Gao Wanlin

  • Affiliations:

  • College of Information and Electrical Engineering, China Agricultural University, Beijing, China;College of Information and Electrical Engineering, China Agricultural University, Beijing, China;College of Information and Electrical Engineering, China Agricultural University, Beijing, China

  • Venue:
  • ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
  • Year:
  • 2005

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Abstract

Ordinal regression is complementary to the standard machine learning tasks of classification and metric regression which goal is to predict variables of ordinal scale. However, every input must be exactly assigned to one of these classes without any uncertainty in standard ordinal regression models. Based on structural risk minimization (SRM) principle, a new support vector learning technique for ordinal regression is proposed, which is able to deal with training data with uncertainty. Firstly, the meaning of the uncertainty is defined. Based on this meaning of uncertainty, two algorithms have been derived. This technique extends the application horizon of ordinal regression greatly. Moreover, the problem about early warning of food security in China is solved by our algorithm.