Neurocomputing
Back-Propagation: Theory, Architecture, and Applications
Back-Propagation: Theory, Architecture, and Applications
Gaussian Processes for Ordinal Regression
The Journal of Machine Learning Research
Evolutionary product unit based neural networks for regression
Neural Networks
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Computers and Electronics in Agriculture
Evaluation Measures for Ordinal Regression
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Supervised ranking in the weka environment
Information Sciences: an International Journal
IEEE Transactions on Evolutionary Computation
An ART-based construction of RBF networks
IEEE Transactions on Neural Networks
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Many real life problems require the classification of items into naturally ordered classes. These problems are traditionally handled by conventional methods intended for the classification of nominal classes, where the order relation is ignored. This paper proposes a hybrid neural network model applied to ordinal classification using a possible combination of projection functions (product unit, PU) and kernel functions (radial basis function, RBF) in the hidden layer of a feed-forward neural network. A combination of an evolutionary and a gradient-descent algorithms is adapted to this model and applied to obtain an optimal architecture, weights and node typology of the model. This combined basis function model is compared to the corresponding pure models: PU neural network, and the RBF neural network. Combined functions using projection and kernel functions are found to be better than pure basis functions for the task of ordinal classification in several datasets.