Diversity measures for multiple classifier system analysis and design

Information Fusion

Volumn 6, Issue 1, 2005, Pages 21-36

  Windeatt, Terry ^a  

^a UNIVERSITY OF SURREY   (United Kingdom)

Author keywords

Binary coding; Decision level fusion; Ensembles; Error correcting; Multiple classifiers

Indexed keywords

BINARY CODES; BOOLEAN FUNCTIONS; COMPUTATIONAL COMPLEXITY; ERROR CORRECTION; INFORMATION DISSEMINATION; LEARNING SYSTEMS; OPTIMIZATION; PATTERN RECOGNITION; PROBABILITY DISTRIBUTIONS;

DECISION LEVEL FUSION; ENSEMBLES; HAMMING DISTANCES; MULTIPLE CLASSIFIER SYSTEMS (MCS);

CLASSIFICATION (OF INFORMATION);

EID: 10444224738     PISSN: 15662535     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.inffus.2004.04.002     Document Type: Article

References (24)

1. L.K. Hansen, P. Salamon, Neural network ensembles, IEEE Transactions on PAMI 12 (10) (1990) 993-1001.
2. T. Windeatt, Vote counting measures for ensemble classifiers, Pattern Recognition 36 (12) (2003) 993-1001.
3. L.I. Kuncheva, C.J. Whitaker, Measures of diversity in classifier ensembles, Machine Learning 51 (2003) 181-207.
4. L.I. Kuncheva, M. Skurichina, R.P.W. Duin, An experimental study on diversity for bagging and boosting with linear classifiers, Information Fusion 3 (2) (2002) 245-258.
5. L.I. Kuncheva, A theoretical study on six classifier fusion strategies, IEEE Transactions on PAMI 24 (2) (2002) 281-286.
6. A.N. Tikhonov, V.A. Arsenin, Solutions of Ill-posed Problems, Winston & Sons, Washington, 1977.
7. T. Windeatt, Recursive partitioning for combining multiple classifiers, Neural Processing Letters 13 (3) (2001) 221-236.
8. S.L. Hurst, D.M. Miller, J. Muzio, Spectral Techniques in Digital Logic, Academic Press, 1985.
9. T. Windeatt, R. Tebbs, Spectral technique for hidden layer neural network training, Pattern Recognition Letters 18 (8) (1997) 723-731.
10. L.I. Kuncheva, That elusive diversity in classifier ensembles, in: Proc. Iberian Conf. on Pattern Recognition and Image Analysis, Mallorca, Spain, Lecture Notes in Computer Science, Springer-Verlag, 2003, pp. 1126-1138.
11. R.E. Schapire, Y. Freund, P. Bartlett, Boosting the Margin: a new explanation for the effectiveness of voting methods, The Annals of Statistics 26 (5) (1998) 1651-1686.
12. V. Koltchinskii, D. Panchenko, Empirical margin distributions and bounding the generalisation error of combined classifiers, The Annals of Statistics 30 (1) (2002) 1-50.
13. G. James, Variance and bias for general loss functions, Machine Learning 51 (2003) 115-135.
14. E.B. Kong, T.G. Dietterich, Error-correcting output coding corrects bias and variance, in: 12th Int. Conf. Machine Learning, San Francisco, 1995, pp. 313-321.
15. L. Breiman, Arcing classifiers, The Annals of Statistics 26 (3) (1998) 801-849.
16. M.S. Kamel, N.M. Wanas, Data dependence in combining classifiers, in: Proc. of 4th Int. Workshop on Multiple Classifier Systems, Guildford, UK, in: T. Windeatt, F. Roli (Eds.), Lecture Notes in Computer Science, Springer-Verlag, 2003, pp. 1-14.
17. C.J. Merz, Using correspondence analysis to combine classifiers, Machine Learning 36 (1-2) (1999) 33-38.
18. L.I. Kuncheva, Switching between selection and fusion in combining classifiers: an experiment, IEEE Transactions on SMC, Part B 32 (2) (2002) 146-156.
19. G. Fumera, F. Roli, Linear combiners for classifier fusion: some theoretical and experimental results, in: Proc. of 4th Int. Workshop on Multiple Classifier Systems, Guildford, UK, in: T. Windeatt, F. Roli (Eds.), Lecture Notes in Computer Science, Springer-Verlag, 2003, pp. 74-83.
20. N. Ueda, Optimal linear combination of neural networks for improving classification performance, IEEE Transactions on PAMI 22 (2) (2000) 207-215.
21. L. Prechelt, Probenl: a set of neural network Benchmark Problems and Benchmarking Rules, Tech. Report 21/94, Univ. Karlsruhe, Germany, 1994.
22. C.J. Merz, P.M. Murphy, UCI repository of machine learning databases, www.ics.uci.edu/~mlearn/MLRepository.html, 1998.
23. T. Windeatt, R. Ghaderi, G. Ardeshir, Spectral coefficients and classifier correlation, in: Proc. of 4th Int. Workshop on Multiple Classifier Systems, Guildford, UK, in: T. Windeatt, F. Roli (Eds.), Lecture Notes in Computer Science, Springer-Verlag, 2003, pp. 276-285.
24. T. Windeatt, R. Ghaderi, Coding and decoding strategies for multiclass learning problems, Information Fusion 4 (1) (2003) 11-21.