Name
Abstract | ||
|---|---|---|
Analysis of the performance of artificial neural networks (ANNs) is usually based on aggregate results on a population of cases. In this paper, we analyze ANN output corresponding to the individual case. We show variability in the outputs of multiple ANNs that are trained and "optimized" from a common set of training cases. We predict this variability from a theoretical standpoint on the basis that multiple ANNs can be optimized to achieve similar overall performance on a population of cases, but produce different outputs for the same individual case because the ANNs use different weights. We use simulations to show that the average standard deviation in the ANN output can be two orders of magnitude higher than the standard deviation in the ANN overall performance measured by the Az value. We further show this variability using an example in mammography where the ANNs are used to classify clustered microcalcifications as malignant or benign based on image features extracted from mammograms. This variability in the ANN output is generally not recognized because a trained individual ANN becomes a deterministic model. Recognition of this variability and the deterministic view of the ANN present a fundamental contradiction. The implication of this variability to the classification task warrants additional study. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/IJCNN.2007.4371360 | Orlando, FL |
Keywords | Field | DocType |
class membership,automated machine classification,uncertainty,receiver operating characteristic,artificial neural networks,image classification,uncertainty handling,two-class classification problems,statistical random variable,random initialization,neural nets,artificial neural network,random variable,roc curve,receiver operator characteristic | Random variable,Finite set,Receiver operating characteristic,Pattern recognition,Computer science,Threshold limit value,Artificial intelligence,Initialization,Artificial neural network,Contextual image classification,Observer (quantum physics),Machine learning | Journal |
ISSN | ISBN | Citations |
1098-7576 E-ISBN : 978-1-4244-1380-5 | 978-1-4244-1380-5 | 6 |
PageRank | References | Authors |
0.59 | 6 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yulei Jiang | 1 | 80 | 8.90 |