  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.
