Learning and Mining using Bagged Augmented Decision Trees (LAMBAST)

Award Information
Agency: Department of Defense
Branch: Army
Contract: W31P4Q-09-C-0279
Agency Tracking Number: A072-018-0170
Amount: $729,828.00
Phase: Phase II
Program: SBIR
Awards Year: 2009
Solicitation Year: 2007
Solicitation Topic Code: A07-018
Solicitation Number: 2007.2
Small Business Information
625 Mount Auburn Street, Cambridge, MA, 02138
DUNS: 115243701
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Ross Eaton
 Scientist
 (617) 491-3474
 reaton@cra.com
Business Contact
 Gail Zaslow
Title: Contracts Specialist
Phone: (617) 491-3474
Email: gzaslow@cra.com
Research Institution
N/A
Abstract
Standoff weapons such as seeker missiles enable strikes at targets of opportunity in limited-access areas while minimizing risk to warfighters. However, seekers cannot be deployed quickly enough for these short-notice, opportunistic missions because of two limitations of their onboard ATR systems: 1) ATRs cannot learn from novel, mission-specific data after initial training is complete; and 2) ATRs cannot identify pertinent, non-redundant information from large training databases. These limitations make it impossible to train a seeker’s ATR in a feasible timeframe for short-notice missions. To remedy these problems and make short-notice seeker missions a reality, we propose Learning and Mining using Bagged Augmented Decision Trees (LAMBAST). LAMBAST examines large databases and extracts sparse, representative subsets of target and clutter samples of interest. For data mining, LAMBAST uses a variant of decision trees, called random decision trees (RDTs). RDTs are immune to overfitting and can incorporate novel, mission-specific data after initial training via perpetual learning. We augment these trees with a distribution modeling component that eliminates redundant information, ignores misrepresentative class distributions in the database, and stops training when decision boundaries are sufficiently sampled. These augmented random decision trees enable fast construction of reliable, mission-specific ATR and make short-notice seeker missions possible.

* Information listed above is at the time of submission. *

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