Handwritten digit recognition with a back-propagation network
Advances in neural information processing systems 2
Metrics for model selection in consumer finance problems
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
A method for improving classification reliability of multilayer perceptrons
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
We consider the problem of learning with the option to reject in graded multiclass problems (GMP), i.e., problems where there exists a gradation among the class labels. We examine two aspects that distinguish GMP from standard multiclass problems: a) the performance metric of interest in GMP may not be strict accuracy; accuracy within a bandwidth of the desired class may be permissible in some situations, and b) the ambiguity expressed by the classifier may simply reflect the underlying ambiguity in the class labeling itself. To deal with these issues, we extend the existing rejection schemes for multiclass problems to cover the GMP case. Firstly, we take the existing rejection schemes and calibrate them to optimize measures other than strict accuracy. Secondly, we present an extension to the well-known dual threshold scheme to deal with GMP. We illustrate these ideas using a credit rating prediction problem.