Added: May 15, 2008 7:12 pm
TECHNOLOGY/BUSINESS OPPORTUNITY
DISCRIMINANT RANDOM FOREST (DRF) CLASSIFICATION METHODOLOGY
Opportunity: Lawrence Livermore National Laboratory (LLNL), operated by the Lawrence Livermore National Security (LLNS), LLC under contract with the U.S. Department of Energy (DOE), is offering the opportunity to commercialize its Discriminant Random Forest, a new nonparametric ensemble classification methodology.
Background: Numerous applications require the ability to discriminate one class of signals, signatures or objects from another based upon a collection of measurable features. State-of-the-art methodologies that perform this type of classification include Support Vector Machines, Neural Networks, and Random Forest. The DRF greatly enhances classification capabilities and supplants the current state-of-the-art.
Description: The Discriminant Random Forest combines advantages of several methodologies and techniques to produce lower classification error rates.
Potential Applications:
The DRF, like its predecessors, is suited to applications requiring discrimination between two classes of interest, such as medical imaging analyses, detection of radiological sources, hidden signal detection, marketing analyses, and intrusion detection for cyber-security. Because DRF produces significantly lower error rates, it may be particularly valuable for applications in which errors can prove costly, such as medical and financial.
Advantages: Significant benefits of the DRF include the following:
|
Feature
|
Benefit
|
|
Produces smaller trees and forests at its peak performance
|
- Consumes less memory
- More efficient training
- Increased analytical capability
- Increased throughput
|
|
Produces stronger forests
|
Improved performance in detection of weak signals/signatures
|
|
Utilizes multiple feature dimensions
|
Yields a more robust and efficient classifier
|
|
Nonparametric methodology
|
Violation of underlying model assumptions is minimized
|
|
Robust to overtraining due to forest size
|
Forests may be grown to arbitrary size to achieve best possible performance
|
Development Status:
A DRF toolbox has been developed and utilized for several real, critical detection applications, such as hidden signal detection. In each case, the DRF achieved superior performance to other approaches, including Support Vector Machines, Cost-Sensitive SVMs, and the conventional Random Forest. Enhancements of the technology are in progress and will be released as subsequent versions. The DRF toolbox is presently command-line driven, but can easily be adapted to a Graphic User Interface (GUI).
LLNL is seeking industry partners with a demonstrated ability to bring such inventions to the market. Moving critical technology beyond the Laboratory to the commercial world helps our licensees gain a competitive edge in the marketplace. All licensing activities are conducted under policies relating to the strict nondisclosure of company proprietary information.
Please visit the IPO website at http://ipo.llnl.gov/workwithus/partneringprocess.php
for more information on working with LLNL and the industrial partnering and technology transfer process.
Note: THIS IS NOT A PROCUREMENT. Companies interested in commercializing LLNL's Discriminant Random Forest Classification Methodology should provide a written statement of interest, which includes the following:
1. Company Name and address.
2. The name, address, and telephone number of a point of contact.
•3. A description of corporate expertise and facilities relevant to commercializing this technology.
Written responses should be directed to:
Lawrence Livermore National Laboratory
Industrial Partnerships Office
P.O. Box 808, L-795
Livermore, CA 94551-0808
Attention: FBO 174-08
Please provide your written statement within thirty (30) days from the date this announcement is published to ensure consideration of your interest in LLNL's Discriminant Random Forest Classification Methodology.
