A neural network approach to ordinal regression

Jianlin Cheng, Zheng Wang, Gianluca Pollastri

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

64 Scopus citations


Ordinal regression is an important type of learning, which has properties of both classification and regression. Here we describe an effective approach to adapt a traditional neural network to learn ordinal categories. Our approach is a generalization of the perceptron method for ordinal regression. On several benchmark datasets, our method (NNRank) outperforms a neural network classification method. Compared with the ordinal regression methods using Gaussian processes and support vector machines, NNRank achieves comparable performance. Moreover, NNRank has the advantages of traditional neural networks: learning in both online and batch modes, handling very large training datasets, and making rapid predictions. These features make NNRank a useful and complementary tool for large-scale data mining tasks such as information retrieval, web page ranking, collaborative filtering, and protein ranking in Bioinformatics. The neural network software is available at: http://www.cs.missouri.edu/~chengji/cheng_software.html.

Original languageEnglish (US)
Title of host publication2008 International Joint Conference on Neural Networks, IJCNN 2008
Number of pages6
StatePublished - 2008
Externally publishedYes
Event2008 International Joint Conference on Neural Networks, IJCNN 2008 - Hong Kong, China
Duration: Jun 1 2008Jun 8 2008

Publication series

NameProceedings of the International Joint Conference on Neural Networks


Other2008 International Joint Conference on Neural Networks, IJCNN 2008
CityHong Kong

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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