Fast feature selection for search models
US11100158B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 30, 2019 |
| Grant date | Aug 24, 2021 |
| Priority date | — |
| Expiry date | Oct 17, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Various embodiments provide for selecting a subset of features to use to train a model for search applications. To select a feature, the candidate features are randomly assigned into two groups. Each of the two groups represents a summation of the respective features that were assigned to it. Then a decision tree building scan is performed on the two groups to determine which of the two groups performs better based a selection criteria. Upon determining which of the two groups is better, the candidate features of the winning group are again randomly assigned into two groups. These two groups are again scanned as described above to determine a winning group. This binary splitting and scanning pattern is continuously performed until the winning group contains one remaining feature. That remaining feature is then designated as a selected feature to be used in the search model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.