US 2024/0256967 A1 · CLASSIFIER TRAINING USING SYNTHETIC TRAINING DATA SAMPLES

Abstract

A classifier is trained to classify business supplier relationships using synthetic training data samples. Real training data samples are collected and transformed into sample encodings using an encoder. The real training data samples include feature data associated with health class indicators indicative of relationships between suppliers and service providers. A set of synthetic training data samples is generated from the sample encodings using a generator and discrimination feedback data is generated using a discriminator based on the real training data samples and the synthetic training data samples. The discrimination feedback data is used to train the generator. A classifier model is trained to classify suppliers with health class indicators using the set of synthetic training data samples. The use of the encoder, generator, and discriminator enables the generation of accurate synthetic training data that represents the source distribution of real data which are often partially observed.

Claims20

Dependent claims

A system comprising:

a processor;
and
a memory comprising computer program code, the memory and the computer program code configured to, with the processor, cause the processor to:
collect a set of real training data samples, wherein the real training data samples include feature data associated with health class indicators, wherein the health class indicators are indicative of a likelihood of a supplier to continue using a service provided by a service provider;
transform the set of real training data samples into sample encodings using an encoder;
generate a set of synthetic training data samples using a generator and the sample encodings;
generate discrimination feedback data of the real training data samples and the synthetic training data samples using a discriminator;
train the generator using the generated discrimination feedback data;
and
train a classifier model to classify suppliers with health class indicators using the set of synthetic training data samples.

7 more elements

A computerized method comprising:

collecting a set of real training data samples, wherein the real training data samples include feature data associated with health class indicators, wherein the health class indicators are indicative of a likelihood of a supplier to continue using a service provided by a service provider;
transforming the set of real training data samples into sample encodings using an encoder;
generating a set of synthetic training data samples using a generator and the sample encodings;
generating discrimination feedback data of the real training data samples and the synthetic training data samples using a discriminator;
training the generator using the generated discrimination feedback data;
and
training a classifier model to classify suppliers with health class indicators using the set of synthetic training data samples.

4 more elements

One or more computer storage media having computer-executable instructions that, upon execution by a processor, cause the processor to at least:

collect a set of real training data samples, wherein the real training data samples include feature data associated with health class indicators, wherein the health class indicators are indicative of a likelihood of a supplier to continue using a service provided by a service provider;
transform the set of real training data samples into sample encodings using an encoder;
generate a set of synthetic training data samples using a generator and the sample encodings;
generate discrimination feedback data of the real training data samples and the synthetic training data samples using a discriminator;
train the generator using the generated discrimination feedback data;
and
train a classifier model to classify suppliers with health class indicators using the set of synthetic training data samples.

4 more elements

Dependencies are inferred from the claim text.

Classifier training using synthetic training data samples

Life of this patent

Classifier training using synthetic training data samples

Life of this patent

Abstract

Claims20