Automated data and label creation for supervised machine learning regression testing
US11295242B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 13, 2019 |
| Grant date | Apr 5, 2022 |
| Priority date | — |
| Expiry date | Dec 9, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Split an input dataset into training and test datasets; the former includes a plurality of data examples, each represented as a feature vector, and having an associated true label. Split the training dataset into a plurality of training data subsets; for each, train a corresponding machine learning model to obtain a plurality of such models, and apply same to the test dataset to obtain a plurality of predicted labels and prediction scores. For each of the plurality of examples, compute an agreement metric based on a corresponding one of the associated true labels; corresponding ones of the predicted labels; and corresponding ones of the prediction scores. Based on the computed metric, select, for at least some of the true label values, appropriate ones of the data examples to be added to a regression set. Add the appropriate ones of the data examples from the test dataset to the regression set.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.