RANKING-BASED TRAINING OF CLASSIFICATION MODEL FOR USE WITH CRITICAL RARE CASES

RANKING-BASED TRAINING OF CLASSIFICATION MODEL FOR USE WITH CRITICAL RARE CASES

Binary classification models can be trained to classify data as being in one of two classes. Membership in a class may be imbalanced so that there are more members in one class than the other. Additionally, one of the classes may have a higher importance than the other, yet appear much less frequent...

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Bibliographische Detailangaben
Hauptverfasser: ZHAI, MENGYAO, MOHAMMADI, KIARASH, ZHAO, HE, TUNG, FREDERICK
Format: Patent
Sprache:eng ; fre
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
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Zusammenfassung:Binary classification models can be trained to classify data as being in one of two classes. Membership in a class may be imbalanced so that there are more members in one class than the other. Additionally, one of the classes may have a higher importance than the other, yet appear much less frequently. It is possible to train the binary classification model using a base loss function and a regularization function based on a ranking of training results in order to reduce the false positives at a high true positive rate.