Figure 4.21. ROCs on fusion of the ensemble designed for Majority rule on the NIST 24 plastic in .NET

Maker QR Code ISO/IEC18004 in .NET Figure 4.21. ROCs on fusion of the ensemble designed for Majority rule on the NIST 24 plastic
Figure 4.21. ROCs on fusion of the ensemble designed for Majority rule on the NIST 24 plastic
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distortion set are compared to the ROC of the Bagging ensemble fusion and the ROC of the best single classi er. And1,OR2,3 : The result of Or fusion of classi ers 2 and 3 is fused with classi er 1 by the And rule.
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Designing Classi ers for Fusion-Based Biometric Veri cation
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gure, it is observed that fusion with the Majority rule ensemble is better than Bagging and the best single classi er. The EER of the Majority rule is 2%. The best decision fusion rule is a combination of Or/And fusion between the classi ers and has an EER of 0.5%. This rule combines classi ers 2 and 3 by the Or rule, the result of which is then combined with classi er 1 by the And rule. This result is reasonable based on the classi er score diversity. Since classi ers 2 and 3 have negative dependence on authentics and positive dependence on impostors, Or fusion is best for them (Or23 ). The authentic conditional dependence between Or23 and classi er 1 will be positive. This is because of the positive correlation coef cient between classi ers 1 and 3. Hence Or1,Or23 = Or123 will not be the best rule. The only other monotonic rule is And1,Or2,3 , which turns out to be the best decision fusion rule.
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CONCLUSIONS
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Statistical dependence has been shown to play a signi cant role in classi er ensemble fusion accuracy. It has been found that Or, And, and Majority decision fusion rules are the important decision fusion rules (for veri cation applications) since one of them is the best decision fusion rule for classi ers of the same accuracy and same pairwise classi er conditional dependence. Analysis of Or rule fusion has been presented to nd the favorable conditional dependence between classi ers that would improve accuracy over conditionally independent classi er fusion with the Or rule. Similar analysis can be done for other major decision fusion rules. The most important contribution of this research has been providing guidelines for designing classi er ensembles that have their output diversity favorable for fusion with the Or, And, and Majority decision fusion rules. Successful design of such classi er ensembles have been demonstrated on biometric data for improving veri cation accuracy. This desirable diversity improves accuracy over not only the best single classi er, but also over fusion of commonly generated ensembles such as Bagging and Boosting, wherever possible.
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ACKNOWLEDGMENT
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This research is funded in part by CyLab at Carnegie Mellon University.
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REFERENCES
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References
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