Systems and methods for generating flexible ensembles of weak learners using broken series training
AssigneeCapital One Services, LLC
InventorMichael LANGFORD
Life of this patent
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Abstract
Systems and methods for novel uses and/or improvements to artificial intelligence applications, particularly in the context of practical applications featuring less complex model architectures. As one example, systems and methods described herein may achieve the technical benefits of a more complex model architecture through an ensemble of less complex models while reducing the overall training burden (e.g., in terms of computing resources, training time, and/or technical feasibility).
Claims20
A system of generating artificial intelligence models featuring flexible ensembles of weak learners using broken series training, the system comprising:
- one or more processors;
- and
- one or more non-transitory, computer readable mediums comprising instructions recorded thereon that, when executed by the one or more processors, cause operations comprising:
- receiving a first dataset for classification using an artificial intelligence model comprising an ensemble of weak learners;
- generating a first feature input based on the first dataset;
- inputting the first feature input into a first weak learner of a plurality of weak learners in the artificial intelligence model, wherein the first weak learner corresponds to a first class of a plurality of weak learner classes;
- receiving a first output from the first weak learner;
- comparing the first output to a first validation metric, wherein the first validation metric is based on the first class;
- adjusting a first weight based on comparing the first output to the first validation metric;
- generating a second feature input based on the first weight and the first output;
- inputting the second feature input into a second weak learner of the plurality of weak learners, wherein the second weak learner corresponds to a second class of a plurality of weak learner classes;
- receiving a second output from the second weak learner;
- comparing the second output to a second validation metric, wherein the second validation metric is based on the second class;
- adjusting a second weight based on comparing the second output to a second validation metric;
- and
- generating, from the artificial intelligence model, a classification for data in the first dataset based on the second weight and the second output.
A method of generating flexible ensembles of weak learners using broken series training, the method comprising:
- receiving a first dataset;
- generating a first feature input based on the first dataset;
- inputting the first feature input into a first weak learner of a plurality of weak learners, wherein the first weak learner corresponds to a first class of a plurality of weak learner classes;
- receiving a first output from the first weak learner;
- comparing the first output to a first validation metric, wherein the first validation metric is based on the first class;
- adjusting a first weight based on comparing the first output to the first validation metric;
- generating, for a second weak learner of the plurality of weak learners, a second feature input based on the first weight and the first output;
- and
- generating a classification for data in the first dataset based on the second feature input.
One or more non-transitory, computer readable mediums comprising instructions recorded thereon that, when executed by one or more processors, cause operations comprising:
- generating a first feature input based on a first dataset;
- inputting the first feature input into a first weak learner of a plurality of weak learners, wherein the first weak learner corresponds to a first class of a plurality of weak learner classes;
- receiving a first output from the first weak learner;
- comparing the first output to a first validation metric, wherein the first validation metric is based on the first class;
- adjusting a first weight based on comparing the first output to the first validation metric;
- and
- generating, for a second weak learner of the plurality of weak learners, a second feature input based on the first weight and the first output.
Dependencies are inferred from the claim text.