Classification Rejection by Prediction

摘要

We address the problem of autonomous decision making in classification of radioastronomy spectrograms from spacecraft. It is known that hte assessment of the decision process can be divided into acceptation of the classification, instant rejection of the current signal classification, or rejection of the entire classifier model. We propose to combine prediction and classification with a double architecture of Time Delay Neural Network (TDNN) to optimize a decision minimizing the false alarm risk. Resutls on real data from URAP experiment aboard Ulysses spacecraft shwo that this scheme is tractable and effective.