Building Ultra-Low False Alarm Rate Support Vector Classifier Ensembles Using Random Subspaces

Conference · Mon Oct 06 04:00:00 EDT 2008

DOI:https://doi.org/10.1109/CIDM.2009.4938622· OSTI ID:951165

Chen, B Y; Lemmond, T D; Hanley, W G

This paper presents the Cost-Sensitive Random Subspace Support Vector Classifier (CS-RS-SVC), a new learning algorithm that combines random subspace sampling and bagging with Cost-Sensitive Support Vector Classifiers to more effectively address detection applications burdened by unequal misclassification requirements. When compared to its conventional, non-cost-sensitive counterpart on a two-class signal detection application, random subspace sampling is shown to very effectively leverage the additional flexibility offered by the Cost-Sensitive Support Vector Classifier, yielding a more than four-fold increase in the detection rate at a false alarm rate (FAR) of zero. Moreover, the CS-RS-SVC is shown to be fairly robust to constraints on the feature subspace dimensionality, enabling reductions in computation time of up to 82% with minimal performance degradation.

• Research Organization: Lawrence Livermore National Laboratory (LLNL), Livermore, CA
• Sponsoring Organization: USDOE
• DOE Contract Number: W-7405-ENG-48
• OSTI ID: 951165
• Report Number(s): LLNL-CONF-407542
• Country of Publication: United States
• Language: English

Related Subjects

42 ENGINEERING
99 GENERAL AND MISCELLANEOUS
ALGORITHMS
DETECTION
FLEXIBILITY
LEARNING
MINING
PERFORMANCE
SAMPLING
VECTORS