Coping with feature error suppression: a mechanism to handle the concept drift

Abstract

Approaches herein relate to reconstructive models such as an autoencoder for anomaly detection. Herein are machine learning techniques that detect and suppress any feature that causes model decay by concept drift. In an embodiment in a production environment, a computer initializes an unsuppressed subset of features with a plurality of features that an already-trained reconstructive model can process. A respective reconstruction error of each feature of the unsuppressed subset of features is calculated. The computer detects that a respective moving average based on the reconstruction error of a particular feature of the unsuppressed subset of features exceeds a respective feature suppression threshold of the particular feature, which causes removal of the particular feature from the unsuppressed subset of features. After removing the particular feature from the unsuppressed subset of features, a loss of the reconstructive model is calculated based on respective reconstruction errors of the unsuppressed subset of features.

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