Constrained classification and ranking via quantiles
US11429894B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 28, 2019 |
| Grant date | Aug 30, 2022 |
| Priority date | — |
| Expiry date | Aug 13, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Example aspects of the present disclosure are directed to systems and methods for learning classification models which satisfy constraints such as, for example, constraints that can be expressed as a predicted positive rate or negative rate on a subset of the training dataset. In particular, through the use of quantile estimators, the systems and methods of the present disclosure can transform a constrained optimization problem into an unconstrained optimization problem that is solved more efficiently and generally than the constrained optimization problem. As one example, the unconstrained optimization problem can include optimizing an objective function where a decision threshold of the classification model is expressed as an estimator of a quantile function on the classification scores of the machine-learned classification model for a subset of the training dataset at a desired quantile.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.