Ordinal regression based on learning vector quantization

Fengzhen Tang, Peter Tiňo

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
263 Downloads (Pure)

Abstract

Recently, ordinal regression, which predicts categories of ordinal scale, has received considerable attention. In this paper, we propose a new approach to solve ordinal regression problems within the learning vector quantization framework. It extends the previous approach termed ordinal generalized matrix learning vector quantization with a more suitable and natural cost function, leading to more intuitive parameter update rules. Moreover, in our approach the bandwidth of the prototype weights is automatically adapted. Empirical investigation on a number of datasets reveals that overall the proposed approach tends to have superior out-of-sample performance, when compared to alternative ordinal regression methods.
Original languageEnglish
Pages (from-to)76-88
JournalNeural Networks
Volume93
Early online date9 May 2017
DOIs
Publication statusPublished - 1 Sept 2017

Keywords

  • Ordinal regression
  • Learning vector quantization
  • Generalized matrix learning vector quantization

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