Machine learning module training using input reconstruction techniques and unlabeled transactions
US11734558B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 12, 2020 |
| Grant date | Aug 22, 2023 |
| Priority date | — |
| Expiry date | Apr 3, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques are disclosed relating to improving machine learning classification using both labeled and unlabeled data, including electronic transactions. A computing system may train a machine learning module using a first set of transactions (of any classifiable data) with label information that indicates designated classifications for those transactions and a second set of transactions without label information. This can allow for improved classification error rates, particularly when additional labeled data may not be present (e.g., if a transaction was disallowed, it may not be later labeled as fraudulent or not). The training process may include generating first error data based on classification results for the first set of transactions, generating second error data based on reconstruction results for both the first and second sets of transactions, and updating the machine learning module based on the first and second error data.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.