Abstract
A mathematical framework for the analysis of critical zones of the input space in a classification problem is introduced. It is based on the definition of uncertainty region, which is the collection of the input patterns whose classification is not certain. Through this definition a characterization of optimal decision functions can be derived.
A general method for detecting the uncertainty region in real-world problems is then proposed, whose implementation can vary according to the connectionist model employed. Its application allows to improve the performance of the resulting neural network.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
L. Devroye, L. Györfi, and G. Lugosi, A Probabilistic Theory of Pattern Recognition. New York: Springer-Verlag (1997).
G. P. Drago and S. Ridella, Possibility and necessity pattern classification using an interval arithmetic perceptron. Neural Computing & Applications, 8 (1999), 40–52.
G. P. Drago and M. Muselli, Support Vector Machines for uncertainty region detection. Submitted to the 12-th Italian Workshop on Neural Nets (2001).
H. Ishibuchi, R. Fujioka, and H. Tanaka, Possibility and necessity pattern classification using neural networks. Fuzzy Sets and Systems, 48 (1992), 331–340.
H. Ishibuchi, H. Tanaka, and H. Okada, An architecture of neural networks with interval weights and its application to fuzzy regression analysis. Fuzzy Sets and Systems, 57 (1993), 27–39.
V. N. Vapnik, Statistical Learning Theory. New York: John Wiley & Sons (1998).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag London Limited
About this paper
Cite this paper
Drago, G.P., Muselli, M. (2002). Detecting uncertainty regions for characterizing classification problems. In: Tagliaferri, R., Marinaro, M. (eds) Neural Nets WIRN Vietri-01. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0219-9_7
Download citation
DOI: https://doi.org/10.1007/978-1-4471-0219-9_7
Publisher Name: Springer, London
Print ISBN: 978-1-85233-505-2
Online ISBN: 978-1-4471-0219-9
eBook Packages: Springer Book Archive