Threshold determination for ARTMAP-FD familiarity discrimination

Author(s): Carpenter, G.A. | Rubin, M.A. | Streilein, W.W. |

Year: 1997

Citation: In C.H. Dagli, M. Akay, O. Ersoy, B.R. Fernandez, & A. Smith (Eds.), Smart Engineering Systems: Neural Networks, Fuzzy Logic, Data Mining, and Evolutionary Programming, 7, (Proceedings of the Artificial Neural Networks in Engineering Conference - ANNIE 97

Abstract: The ARTMAP-FD neural network performs both identification (placing test patterns in classes encountered during training) and familiarity discrimination (judging whether a test pattern belongs to any of the classes encountered during training). ARTMAP-FD quantifies the familiarity of a test pattern by computing a measure of the degree to which the pattern?s components lie within the ranges of values of training patterns grouped in the same cluster. This familiarity measure is compared to a threshold which can be varied to generate a receiver operating characteristic (ROC) curve. Methods for selecting optimal values for the threshold are evaluated. The performance of validation-set methods is compared with that of methods which track the development of the network?s discrimination capability during training. The techniques are applied to databases of simulated radar range profiles.

Topics: Machine Learning, Applications: Remote Sensing, Models: ARTMAP, Modified ART,

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