Scalable streaming decision tree learning
US10664756B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 30, 2015 |
| Grant date | May 26, 2020 |
| Priority date | — |
| Expiry date | Apr 15, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N5/025
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In one embodiment, a computer-implemented method includes receiving training data including a plurality of records, each record having a plurality of attributes. The training data is horizontally parallelized across two or more processing elements. This horizontal parallelizing includes dividing the training data into two or more subsets of records; assigning each subset of records to a corresponding processing element of the two or more processing elements; transmitting each subset of records to its assigned processing element; and sorting, at the two or more processing elements, the two or more subsets of records to two or more candidate leaves of a decision tree. The output from horizontally parallelizing is converted into input for vertically parallelizing the training data. The training data is vertically parallelized across the two or more processing elements. The decision tree is grown based at least in part on the horizontally parallelizing, the converting, and the vertically parallelizing.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.