Title :
How to “secure” the decisions of a NN classifier
Author :
Decaestecker, Christine ; Van de Merckt, Thierry
Author_Institution :
IRIDIA, Univ. Libre de Bruxelles, Belgium
fDate :
27 Jun-2 Jul 1994
Abstract :
The paper presents an approach to give an introspective capacity to a neural net (NN) classifier. More precisely, a strategy is developed to detect “dangerous” areas in the pattern space where the decisions can be erroneous. In these areas the classification is hazardous and it is preferable not to take a decision. Our approach is detailed for a NN using prototypes named NNP and is based on a geometrical interpretation of the concept representations generated by the NN classifier. Experiments show the advantages of this approach in presence of nonlinear class boundaries, class overlapping and noise
Keywords :
computational geometry; decision theory; feedforward neural nets; pattern classification; NN classifier; NNP; class overlapping; concept representations; geometrical interpretation; introspective capacity; neural net classifier; nonlinear class boundaries; pattern space; three-layer fully connected feedforward net; Bayesian methods; Euclidean distance; Multilayer perceptrons; Neural networks; Partitioning algorithms; Prototypes; Radial basis function networks; Temperature dependence; Training data; Zirconium;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374172
