Method for generating rulesets using tree-based models for black-box machine learning explainability
US11531915B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 20, 2019 |
| Grant date | Dec 20, 2022 |
| Priority date | — |
| Expiry date | Jan 4, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Herein are techniques to generate candidate rulesets for machine learning (ML) explainability (MLX) for black-box ML models. In an embodiment, an ML model generates classifications that each associates a distinct example with a label. A decision tree that, based on the classifications, contains tree nodes is received or generated. Each node contains label(s), a condition that identifies a feature of examples, and a split value for the feature. When a node has child nodes, the feature and the split value that are identified by the condition of the node are set to maximize information gain of the child nodes. Candidate rules are generated by traversing the tree. Each rule is built from a combination of nodes in a tree traversal path. Each rule contains a condition of at least one node and is assigned to a rule level. Candidate rules are subsequently optimized into an optimal ruleset for actual use.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.