US 2022/0027797 A1Published application

Hybrid data chunk continuous machine learning

AssigneeInternational Business Machines Corporation

InventorsLEI TIAN, Si Er Han, Dong Hai Yu, Liang Wang, YI SHAO, Chun Lei Xu

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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.

Claims20

Dependent claims

A computer-implemented method comprising:

creating, by one or more computer processors, 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;
calculating, by one or more computer processors, a set of weights for each component model in the EMS, wherein each set of weights comprises a current model weight (CMW) and an accumulated model weight (AMW);
responsive to detecting model drift, creating, by one or more computer processors, a new component model based on a new hybrid data chunk and adding the new component model to the EMS;
recalculating, by one or more computer processors, the set of weights for each component model in the EMS utilizing one or more evaluation techniques compared against the new hybrid data chunk;
and
predicting, by one or more computer processors, 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, wherein the predictions from each component model in the set of selected top component models associated with higher weights are given greater deference when aggregating the predictions.

4 more elements

A computer program product comprising:

one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the stored program instructions comprising:
program instructions to 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;
program instructions to calculate a set of weights for each component model in the EMS, wherein each set of weights comprises a current model weight (CMW) and an accumulated model weight (AMW);
program instructions to 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;
program instructions to 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;
and
program instructions to 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, wherein the predictions from each component model in the set of selected top component models associated with higher weights are given greater deference when aggregating the predictions.

5 more elements

A computer system comprising:

one or more computer processors;
one or more computer readable storage media;
and
program instructions stored on the computer readable storage media for execution by at least one of the one or more processors, the stored program instructions comprising:
program instructions to 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;
program instructions to calculate a set of weights for each component model in the EMS, wherein each set of weights comprises a current model weight (CMW) and an accumulated model weight (AMW);
program instructions to 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;
program instructions to 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;
and
program instructions to 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, wherein the predictions from each component model in the set of selected top component models associated with higher weights are given greater deference when aggregating the predictions.

8 more elements

Dependencies are inferred from the claim text.