Abstract
Statistical dependence between classifier scores has been shown to affect the verification accuracy for certain decision fusion rules (e.g.,‘majority’, ‘and’, ‘or’). In this paper, we investigate what are the best decision fusion rules for various statistical dependences between classifiers and check whether the best accuracy depends on the statistical dependence. This is done by evaluating accuracy of decision fusion rules on three jointly Gaussian scores with various covariances. It is found that the best decision fusion rule for any given statistical dependence is one of the three major rules – ‘majority’,‘and’, ‘or’. The correlation coefficient between the classifier scores can be used to predict the best decision fusion rule, as well as for evaluation of how well-designed the classifiers are. This can be applied to biometric verification; and it is shown using the NIST 24 fingerprint database and the AR face database that the prediction and evaluation agree.
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References
Kuncheva, L.I., Whitaker, C.J., Shipp, C.A., Duin, R.P.W.: Limits on the majority vote accuracy in classifier fusion. Pattern Analysis and Applications 6, 22–31 (2003)
Demikelker, M., Altincay, H.: Plurality voting-based multiple classifier systems: statistically independent with respect to dependent classifier sets. Pattern Recognition 35, 2365–2379 (2002)
Venkataramani, K., Vijaya Kumar, B.V.K.: Conditionally dependent classifier fusion using and rule for improved biometric verification. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds.) ICAPR 2005. LNCS, vol. 3687, pp. 277–286. Springer, Heidelberg (2005)
Venkataramani, K., VijayaKumar, B.V.K.: Or rule fusion of conditionally dependent correlation filter based classifiers for improved biometric verification. In: OPR XVII, Proc. SPIE, vol. 6245, pp. 105–116 (2006)
Liu, Y., Yao, X.: Ensemble learning via negative correlation. Neural Networks 12 (1999)
Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles. Machine Learning 51, 181–207 (2003)
Watson, C.I.: NIST special database 24 - live-scan digital video fingerprint database (1998)
Martinez, A.M., Benavente, R.: The AR face database. Technical Report 24, CVC (1998)
Varshney, P.K.: Distributed detection and data fusion. Springer, New York (1997)
VijayaKumar, B.V.K., Carlson, D.W., Mahalanobis, A.: Optimal trade-off synthetic discriminant function filters for arbitrary devices. Optics Letters 19, 1556–1558 (1994)
Venkataramani, K., VijayaKumar, B.V.K.: Performance of composite correlation filters in fingerprint verification. Optical Engineering 43, 1820–1827 (2004)
Hastie, T., et al.: The elements of statistical learning, 1st edn. Springer, Heidelberg (2003)
Zheng, W., Zou, C., Zhao, L.: Real-time face recognition using Gram-Schmidt orthogonalization for LDA. In: Int. Conf. on Pattern Recognition, pp. 403–406 (2004)
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Venkataramani, K., Kumar, B.V.K.V. (2006). Role of Statistical Dependence Between Classifier Scores in Determining the Best Decision Fusion Rule for Improved Biometric Verification. In: Gunsel, B., Jain, A.K., Tekalp, A.M., Sankur, B. (eds) Multimedia Content Representation, Classification and Security. MRCS 2006. Lecture Notes in Computer Science, vol 4105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11848035_65
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DOI: https://doi.org/10.1007/11848035_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-39392-4
Online ISBN: 978-3-540-39393-1
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