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US 2023/0419190 A1Published application
Training ensemble models to improve performance in the presence of unreliable base classifiers
AssigneeWestern Digital Technologies, Inc.
InventorsYongjune KIM, Yuval CASSUTO
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Abstract
A system and method for training base classifiers in a boosting algorithm includes optimally training base classifiers considering an unreliability model, and then using a scheme with an aggregator decoder that reverse-flips inputs using inter-classifier redundancy introduced in training.
Claims20
A method of training a machine learning model, comprising:
- training a plurality of base classifiers of an ensemble model in a sequence of training iterations to minimize an expected loss function, wherein each training iteration of the sequence of training iterations is associated with one base classifier of the plurality of base classifiers;
- determining a plurality of base classifier weights for the plurality of base classifiers, wherein each base classifier weight of the plurality of base classifier weights is associated with one base classifier of the plurality of base classifiers;
- training a plurality of redundant base classifiers of the ensemble model based on the plurality of trained base classifiers;
- determining a plurality of redundant base classifier weights for the plurality of redundant base classifiers, wherein each redundant base classifier weight of the plurality of redundant base classifier weights is associated with one redundant base classifier of the plurality of redundant base classifiers;
- and
- updating the ensemble model with the plurality of base classifier weights and the plurality of redundant base classifier weights.
3 more elements
A processing system configured to train a machine learning model, comprising:
- a memory comprising computer-executable instructions;
- one or more processors configured to execute the computer-executable instructions and cause the processing system to:
- train a plurality of base classifiers of an ensemble model in a sequence of training iterations to minimize an expected loss function, wherein each training iteration of the sequence of training iterations is associated with one base classifier of the plurality of base classifiers;
- determine a plurality of base classifier weights for the plurality of base classifiers, wherein each base classifier weight of the plurality of base classifier weights is associated with one base classifier of the plurality of base classifiers;
- train a plurality of redundant base classifiers of the ensemble model based on the plurality of trained base classifiers;
- determine a plurality of redundant base classifier weights for the plurality of redundant base classifiers, wherein each redundant base classifier weight of the plurality of redundant base classifier weights is associated with one redundant base classifier of the plurality of redundant base classifiers;
- and
- update the ensemble model with the plurality of base classifier weights and the plurality of redundant base classifier weights.
5 more elements
A method for classifying with a machine learning model, comprising:
- receiving at a decoder of an ensemble model an input vector comprising output from a plurality of base classifiers and a redundant base classifier trained based on the plurality of base classifiers;
- decoding the input vector with the decoder to generate input data for an aggregator of the ensemble model;
- providing the input data to the aggregator;
- and
- generating, by the aggregator, a classification for the input vector based on the input data.
2 more elements
A processing system configured to classify with a machine learning model, comprising:
- a memory comprising computer-executable instructions;
- one or more processors configured to execute the computer-executable instructions and cause the processing system to:
- receive at a decoder of an ensemble model an input vector comprising output from a plurality of base classifiers and a redundant base classifier trained based on the plurality of base classifiers;
- decode the input vector with the decoder to generate input data for an aggregator of the ensemble model;
- provide the input data to the aggregator;
- and
- generate, by the aggregator, a classification for the input vector based on the input data.
4 more elements
Dependencies are inferred from the claim text.