Building Ultra-Low False Alarm Rate Support Vector Classifier Ensembles Using Random Subspaces
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 Lab. (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- W-7405-ENG-48
- OSTI ID:
- 951165
- Report Number(s):
- LLNL-CONF-407542; TRN: US200911%%359
- Resource Relation:
- Conference: Presented at: 2009 Symposium on Computational Intelligence and Data Mining, Nashville, TN, United States, Mar 30 - Apr 02, 2009
- Country of Publication:
- United States
- Language:
- English
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