Many real life problems require the classification of items in naturally ordered classes. These problems are traditionally handled by conventional methods for nominal classes, ignoring the order. This paper introduces a new training model for feedforward neural networks, for multiclass classification problems, where the classes are ordered. The proposed model has just one output unit which takes values in the interval [0,1]; this interval is then subdivided into K subintervals (one for each class), according to a specific probabilistic model. A comparison is made with conventional approaches, as well as with other architectures specific for ordinal data proposed in the literature. The new model compares favourably with the other methods under study, in the synthetic dataset used for evaluation.
[1]
Amnon Shashua,et al.
Ranking with Large Margin Principle: Two Approaches
,
2002,
NIPS.
[2]
Klaus Obermayer,et al.
Regression Models for Ordinal Data: A Machine Learning Approach
,
1999
.
[3]
Luc De Raedt,et al.
Machine Learning: ECML 2001
,
2001,
Lecture Notes in Computer Science.
[4]
Mario Costa,et al.
Probabilistic Interpretation of Feedforward Network Outputs, with Relationships to Statistical Prediction of Ordinal Quantities
,
1996,
Int. J. Neural Syst..
[5]
Eibe Frank,et al.
A Simple Approach to Ordinal Classification
,
2001,
ECML.