System and method for mining time-changing data streams
US7565369B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 28, 2004 |
| Grant date | Jul 21, 2009 |
| Priority date | — |
| Expiry date | Jan 24, 2025 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY10S707/99945
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A general framework for mining concept-drifting data streams using weighted ensemble classifiers. An ensemble of classification models, such as C4.5, RIPPER, naive Bayesian, etc., is trained from sequential chunks of the data stream. The classifiers in the ensemble are judiciously weighted based on their expected classification accuracy on the test data under the time-evolving environment. Thus, the ensemble approach improves both the efficiency in learning the model and the accuracy in performing classification. An empirical study shows that the proposed methods have substantial advantage over single-classifier approaches in prediction accuracy, and the ensemble framework is effective for a variety of classification models.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.