International Business Machines Corporation
HYBRID DATA CHUNK CONTINUOUS MACHINE LEARNING

Abstract:

In an approach to mitigating model drift, one or more computer processors create an ensemble model set (EMS) comprising one or more component models, wherein each component model in the EMS is trained utilizing at least one of the following: a distinct algorithm, technique, and hybrid data chunk; calculate a set of weights for each component model in the EMS; responsive to detecting model drift, create a new component model based on a new hybrid data chunk and adding the new component model to the EMS; recalculate the set of weights for each component model in the EMS utilizing one or more evaluation techniques compared against the new hybrid data chunk; predict a final prediction by aggregating one or more predictions from one or more component models in a set of selected top component models between subsequent model drifts.