Uncertainty quantification for machine learning classification modelling
US12229691B2 · kind B2 · utility
Assignee
Inventor
Key dates
| Filing date | Mar 16, 2023 |
| Grant date | Feb 18, 2025 |
| Priority date | — |
| Expiry date | Mar 16, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Certain aspects of the disclosure provide a method, comprising: processing input data with an ensemble of nonlinear machine learning models; generating a sparse high-dimensional embedding based on one or more leaf nodes of each nonlinear machine learning model in the ensemble of nonlinear machine learning models; projecting the high-dimensional embedding into a lower-dimensional embedding, wherein the lower-dimensional embedding is less sparse than the high-dimensional embedding; processing the lower-dimensional embedding with a linear machine learning model to generate a binary class prediction; determining a confidence for the binary class prediction; and outputting: the binary class prediction if the confidence is greater than or equal to a threshold; or a flipped binary class prediction if the confidence is lower than the threshold.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.