An experimental study on diversity for bagging and boosting with linear classifiers
Tài liệu tham khảo
Bauer, 1999, An empirical comparison of voting classification algorithms: Bagging, boosting, and variants, Machine Learning, 36, 105, 10.1023/A:1007515423169
Breiman, 1996, Bagging predictors, Machine Learning, 26, 123, 10.1007/BF00058655
Breiman, 1998, Arcing classifiers, The Annals of Statistics, 26, 801
Breiman, 1999, Combining predictors, 31
P. Cunningham, J. Carney. Diversity versus quality in classification ensembles based on feature selection, Technical Report TCD-CS-2000-02, Department of Computer Science, Trinity College Dublin, 2000
Dietterich, 2000, Ensemble methods in machine learning, vol. 1857, 1
Efron, 1993
Freund, 1997, A decision-theoretic generalization of on-line learning and an application to boosting, Journal of Computer and System Sciences, 55, 119, 10.1006/jcss.1997.1504
Freund, 1998, Discussion of the paper Arcing Classifiers by Leo Breiman, The Annals of Statistics, 26, 824
Giacinto, 2001, Design of effective neural network ensembles for image classification processes, Image Vision and Computing Journal, 19, 699, 10.1016/S0262-8856(01)00045-2
Golomb, 1963, The search for Hadamard matrices, American Mathematics Monthly, 70, 12, 10.2307/2312777
Hansen, 1990, Neural network ensembles, IEEE Transactions on Pattern Analysis and Machine Intelligence, 12, 993, 10.1109/34.58871
S. Hashem, B. Schmeiser, Y. Yih, Optimal linear combinations of neural networks: an overview, in: IEEE International Conference on Neural Networks, Orlando, Florida, 1994, pp. 1507–1512
Ho, 1998, The random space method for constructing decision forests, IEEE Transactions on Pattern Analysis and Machine Intelligence, 20, 832, 10.1109/34.709601
Kohavi, 1996, Bias plus variance decomposition for zero-one loss functions, 275
Krogh, 1995, Neural network ensembles, cross validation and active learning, vol. 7, 231
Kuncheva, 2001, Using measures of similarity and inclusion for multiple classifier fusion by decision templates, Fuzzy Sets and Systems, 122, 401, 10.1016/S0165-0114(99)00161-X
Kuncheva, 2002, A theoretical study on expert fusion strategies, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 281, 10.1109/34.982906
Kuncheva, 2001, Decision templates for multiple classifier fusion: an experimental comparison, Pattern Recognition, 34, 299, 10.1016/S0031-3203(99)00223-X
Kuncheva, 2001, Ten measures of diversity in classifier ensembles: limits for two classifiers, 10/1
L.I. Kuncheva, C.J. Whitaker. Measures of diversity in classifier ensembles. Machine Learning, in press
L.I. Kuncheva, C.J. Whitaker, C.A. Shipp, R.P.W. Duin, Is independence good for combining classifiers?, in: Proc. 15th International Conference on Pattern Recognition, Barcelona, Spain, vol. 2, 2000, pp. 169–171
Lam, 2000, Classifier combinations: implementations and theoretical issues, vol. 1857, 78
Y. Liu, X. Yao, Negatively correlated neural networks for classification, in: Proc. 3rd International Symposium on Artificial Life and Robotics (AROBIII’98), Japan, 1998, pp. 736–739
Y. Liu, X. Yao, Simultaneous learning of negatively correlated neural network, in: Proc 9th Australian Conference on Neural Networks (ACNN’98), Brisbane, Australia, 1998, pp. 183–187
Liu, 1999, Ensemble learning via negative correlation, Neural Networks, 12, 1399, 10.1016/S0893-6080(99)00073-8
D. Partridge, W. Krzanowski, Distinct failure diversity in multiversion software, personal communication
Partridge, 1997, Software diversity: practical statistics for its measurement and exploitation, Information and Software Technology, 39, 707, 10.1016/S0950-5849(97)00023-2
Rosen, 1996, Ensemble learning using decorrelated neural networks, Connection Science, 8, 373, 10.1080/095400996116820
Ruta, 2001, Application of the evolutionary algorithms for classifier selection in multiple classifier systems with majority voting, vol. 2096
D.B. Skalak, The sources of increased accuracy for two proposed boosting algorithms, in: Proc. American Association for Artificial Intelligence, AAAI-96, Integrating Multiple Learned Models Workshop, 1996
M. Skurichina, Stabilizing Weak Classifiers, Ph.D. thesis, Delft University of Technology, Delft, The Netherlands, 2001
Sneath, 1973
Tumer, 1996, Error correlation and error reduction in ensemble classifiers, Connection Science, 8, 385, 10.1080/095400996116839
Tumer, 1999, Linear and order statistics combiners for pattern classification, 127
Xu, 1992, Methods of combining multiple classifiers and their application to handwriting recognition, IEEE Transactions on Systems, Man, and Cybernetics, 22, 418, 10.1109/21.155943
Yule, 1900, On the association of attributes in statistics, Philos. Trans., A, 194, 257, 10.1098/rsta.1900.0019