Bayesian tree aggregation in decision forests to increase detection of rare malware
US10728271B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 11, 2019 |
| Grant date | Jul 28, 2020 |
| Priority date | — |
| Expiry date | Jun 11, 2039 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L69/22
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In one embodiment, a computing device provides a feature vector as input to a random decision forest comprising a plurality of decision trees trained using a training dataset, each decision tree being configured to output a classification label prediction for the input feature vector. For each of the decision trees, the computing device determines a conditional probability of the decision tree based on a true classification label and the classification label prediction from the decision tree for the input feature vector. The computing device generates weightings for the classification label predictions from the decision trees based on the determined conditional probabilities. The computing device applies a final classification label to the feature vector based on the weightings for the classification label predictions from the decision trees.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.