A Neural Network Approach To Ordinal Regression

# A neural network approach to ordinal regression

**arXiv ID:** 0704.1028
**Authors:** Jianlin Cheng
**Published:** 2007-04-08T17:36:00Z
**Abstract:**
Ordinal regression is an important type of learning, which has properties of both classification and regression. Here we describe a simple and 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 processing tasks such as information retrieval, web page ranking, collaborative filtering, and protein ranking in Bioinformatics.

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## References

- [arXiv:0704.1028](http://arxiv.org/abs/0704.1028v1)