METHODS AND SYSTEMS FOR GENERATING RECOMMENDATIONS FOR COUNTERFACTUAL EXPLANATIONS OF COMPUTER ALERTS THAT ARE AUTOMATICALLY DETECTED BY A MACHINE LEARNING ALGORITHM

Methods and systems are described herein for generating recommendations for counterfactual explanations to computer alerts that are automatically detected by a machine learning algorithm. The methods and systems use an artificial neural network architecture that trains a hybrid classifier and autoen...

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Bibliographic Details
Main Authors BARR, Brian, WITTENBACH, Jason
Format Patent
LanguageEnglish
Published 30.06.2022
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Summary:Methods and systems are described herein for generating recommendations for counterfactual explanations to computer alerts that are automatically detected by a machine learning algorithm. The methods and systems use an artificial neural network architecture that trains a hybrid classifier and autoencoder. For example, one model (or artificial neural network), which is a classifier, is trained to make predictions. A second model (or artificial neural network), which is an autoencoder, is trained to reconstruct its inputs. As the second model is trained to reconstruct its inputs means, the second model is implicitly trained to determine what in-sample data looks like. By combining these networks and train them jointly, the system generates predictions (e.g., counterfactual explanations) that are in-sample.
Bibliography:Application Number: US202017138890