US 2024/0193489 A1Published application

Machine-learning method for automatic instance assignment under fairness and capacity constraints

AssigneeFeedzai—Consultadoria e Inovação Tecnológica, S.A.

InventorsDiogo MOREIRA DA SILVA EIRA LEITÃO, PEDRO DOS SANTOS SALEIRO, PEDRO GUSTAVO SANTOS RODRIGUES BIZARRO

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Abstract

A computer-implemented machine-learning training method and system are provided. Training instance(s) can be input to a first machine-learning model and, in response, for each instance, classification prediction output(s) and error prediction output(s) received, which can include an output representing one or more of a false-negative, a false-positive, a true-negative, and a true-positive. The instance(s), classification prediction output(s), and identifier(s) are input to a second machine-learning model and, in response, a predicted error output that includes one or more of a predicting false-negative, a false-positive, a true-negative, and a true-positive. The training instance(s) are assigned to the constraint-unlimited machine-learning classifier or the one or more constraint-limited classifiers, thereby minimizing the composite loss function.

Claims20

Dependent claims

A computer-implemented machine-learning method, the method comprising:

receiving, by one or more computing devices, one or more training instances for training a target machine-learning model;
inputting, by the one or more computing devices to a first machine-learning model, the received one or more training instances;
receiving, by the one or more computing devices from the first machine-learning model, for each of the one or more training instances, one or more respective classification prediction outputs and one or more respective error prediction outputs, wherein the one or more respective error prediction outputs of the first machine-learning model include an output representing one or more of a false-negative, a false-positive, a true-negative, and a true-positive;
inputting, by the one or more computing devices to a second machine-learning model, the received one or more training instances, the one or more respective classification prediction outputs, and one or more identifiers of one or more constraint-limited classifiers;
receiving, by the one or more computing devices from the second machine-learning model, for each of the received one or more training instances and each of the one or more constraint-limited classifiers, one or more respective error prediction outputs wherein the one or more respective error prediction outputs of the second machine-learning model include an output representing one or more of a false-negative, a false-positive, a true-negative, and a true-positive;
obtaining, by the one or more computing devices, a composite loss-function from:
a loss function for a constraint-unlimited machine-learning classifier using the one or more respective error prediction outputs for the received one or more training instances from the first machine-learning model;
and
a loss function for each of the one or more constraint-limited classifiers using the predicted error for the received one or more training instances from the second machine-learning model;
and
assigning, by the one or more computing devices, the received one or more training instances to the constraint-unlimited machine-learning classifier or to one or more constraint-limited classifiers, thereby minimizing the composite loss function.

8 more elements

A computer-implemented machine-learning training system, the system comprising:

at least one computing device, configured to access instructions stored on non-transitory processor readable media, wherein the instructions, when executed by the at least one computing device cause the at least one computing device to:
receive one or more training instances for training a target machine-learning mode;
input to a first machine-learning model, the received one or more training instances;
receive from the first machine-learning model, for each of the one or more training instances, one or more respective classification prediction outputs and one or more respective error prediction outputs, wherein the one or more respective error prediction outputs of the first machine-learning model include an output representing one or more of a false-negative, a false-positive, a true-negative, and a true-positive;
input to a second machine-learning model, the received one or more training instances, the one or more respective classification prediction outputs, and one or more identifiers of one or more constraint-limited classifiers;
receive from the second machine-learning model, for each of the received one or more training instances and each of the one or more constraint-limited classifiers, one or more respective error predicted outputs wherein the one or more respective error prediction outputs of the second machine-learning model include an output representing one or more of a false-negative, a false-positive, a true-negative, and a true-positive;
obtain a composite loss-function from:
a loss function for a constraint-unlimited machine-learning classifier using the one or more respective error prediction outputs for the received one or more training instances from the first machine-learning model;
and
a loss function for each of the one or more constraint-limited classifiers using the predicted error for the received one or more training instances from the second machine-learning model;
and
assign the received one or more training instances, to the constraint-unlimited machine-learning classifier or to one or more constraint-limited classifiers, thereby minimizing the composite loss function.

9 more elements

Dependencies are inferred from the claim text.