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 5 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 10 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.

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