Fast and accurate anomaly detection explanations with forward-backward feature importance
US11966275B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 22, 2022 |
| Grant date | Apr 23, 2024 |
| Priority date | — |
| Expiry date | Nov 22, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/088
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present invention relates to machine learning (ML) explainability (MLX). Herein are local explanation techniques for black box ML models based on coalitions of features in a dataset. In an embodiment, a computer receives a request to generate a local explanation of which coalitions of features caused an anomaly detector to detect an anomaly. During unsupervised generation of a new coalition, a first feature is randomly selected from features in a dataset. Which additional features in the dataset can join the coalition, because they have mutual information with the first feature that exceeds a threshold, is detected. For each feature that is not in the coalition, values of the feature are permuted in imperfect copies of original tuples in the dataset. An average anomaly score of the imperfect copies is measured. Based on the average anomaly score of the imperfect copies, a local explanation is generated that references (e.g. defines) the coalition.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.