In the pattern recognition literature, Huang and Suen introduced the “multinomial” rule for fusion of multiple classifiers under the name of Behavior Knowledge Space (BKS) method [1]. This classifier fusion method can provide very good performances if large and representative data sets are available. Otherwise over fitting is likely to occur, and the generalization error quickly increases. In spite of this crucial small sample size problem, analytical models of BKS generalization error are currently not available. In this paper, the generalization error of BKS method is analysed, and a simple analytical model that relates error to sample size is proposed. In addition, a strategy for improving performances by using linear classifiers in “ambiguous” cells of BKS table is described. Preliminary experiments on synthetic and real data sets are reported.
The Behavior Knowledge Space Fusion Method: Analysis of Generalization Error and Strategies for Performance Improvement
ROLI, FABIO
2003-01-01
Abstract
In the pattern recognition literature, Huang and Suen introduced the “multinomial” rule for fusion of multiple classifiers under the name of Behavior Knowledge Space (BKS) method [1]. This classifier fusion method can provide very good performances if large and representative data sets are available. Otherwise over fitting is likely to occur, and the generalization error quickly increases. In spite of this crucial small sample size problem, analytical models of BKS generalization error are currently not available. In this paper, the generalization error of BKS method is analysed, and a simple analytical model that relates error to sample size is proposed. In addition, a strategy for improving performances by using linear classifiers in “ambiguous” cells of BKS table is described. Preliminary experiments on synthetic and real data sets are reported.
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