A probabilistic model of classifier competence for dynamic ensemble selection

Pattern Recognition

Volumn 44, Issue 10-11, 2011, Pages 2656-2668

  Woloszynski, Tomasz ^a   Kurzynski, Marek ^a  

^a WROCLAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Beta distribution; Classifier competence; Multiple classifier system; Probabilistic modelling

Indexed keywords

BETA DISTRIBUTIONS; DATA SETS; DYNAMIC CLASSIFIER SELECTION; DYNAMIC ENSEMBLE; EXPECTED VALUES; FEATURE SPACE; FEATURE VECTORS; MULTIPLE CLASSIFIER SYSTEMS; POTENTIAL FUNCTION; PROBABILISTIC MODELLING; PROBABILISTIC MODELS; THREE SYSTEMS;

RANDOM VARIABLES;

PROBABILITY DISTRIBUTIONS;

EID: 79958833224     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2011.03.020     Document Type: Article

References (55)

1. W. Khreich, E. Granger, A. Miri, and R. Sabourin Iterative Boolean combination of classifiers in the ROC space: an application to anomaly detection with HMMs Pattern Recognition 43 2010 2732 2752
2. R. Polikar, J. DePasquale, H.S. Mohammed, G. Brown, and L.I. Kuncheva Learn.MF: a random subspace approach for the missing feature problem Pattern Recognition 43 2010 3817 3832
3. M. Ghannad-Rezaie, H. Soltanian-Zadeh, H. Ying, and M. Dong Selection-fusion approach for classification of datasets with missing values Pattern Recognition 43 2010 2340 2350
4. T. Woloszynski, and M. Kurzynski Application of combining classifiers using dynamic weights to the protein secondary structure prediction - comparative analysis of fusion methods Biological and Medical Data Analysis, Lecture Notes in Computer Science 2006 83 91 (Pubitemid 47163844)
5. T.G. Dietterich Ensemble methods in machine learning Proceedings of the International Workshop on Multiple Classifier Systems, Lecture Notes in Computer Science 2000 1 15
6. L.I. Kuncheva Combining Pattern Classifiers: Methods and Algorithms 2004 Wiley-Interscience
7. Y. Freund, Y. Mansour, and R.E. Schapire Why averaging classifiers can protect against overfitting Eighth International Workshop on Artificial Intelligence and Statistics 2001
8. L.I. Kuncheva A theoretical study on six classifier fusion strategies IEEE Transactions on Pattern Analysis and Machine Intelligence 24 2 2002 281 286 (Pubitemid 34198215)
9. J. Kittler, M. Hatef, R.P.W. Duin, and J. Matas On combining classifiers IEEE Transactions on Pattern Analysis and Machine Intelligence 20 3 1998 226 239 (Pubitemid 128741312)
10. L. Breiman Bagging predictors Machine Learning 24 2 1996 123 140 (Pubitemid 126724382)
11. Y. Freund, and R.E. Schapire A decision-theoretic generalization of on-line learning and an application to boosting Journal of Computer and System Sciences 55 1 1997 119 139 (Pubitemid 127433398)
12. T.K. Ho The random subspace method for constructing decision forests IEEE Transactions on Pattern Analysis and Machine Intelligence 20 8 1998 832 844 (Pubitemid 128741345)
13. D. Ruta, and B. Gabrys Classifier selection for majority voting Information Fusion 6 1 2005 63 81
14. A.M.P. Canuto, M.C.C. Abreu, L.M. Oliveira, J.C. Xavier Jr., and A.M. Santos Investigating the influence of the choice of the ensemble members in accuracy and diversity of selection-based and fusion-based methods for ensembles Pattern Recognition Letters 28 4 2007 472 486 (Pubitemid 44918135)
15. Z.-H. Zhou, J. Wu, and W. Tang Ensembling neural networks: many could be better than all Artificial Intelligence 137 12 2002 239 263 (Pubitemid 34405220)
16. G. Martinez-Munoz, D. Hernandez-Lobato, and A. Suarez An analysis of ensemble pruning techniques based on ordered aggregation IEEE Transactions on Pattern Analysis and Machine Intelligence 31 2 2009 245 259
17. D.D. Margineantu, and T.G. Dietterich Pruning adaptive boosting Fourteenth International Conference on Machine Learning 1997 211 218
18. G. Martinez-Munoz, and A. Suarez Aggregation ordering in bagging IASTED International Conference on Artificial Intelligence and Applications 2004 258 263 (Pubitemid 40027393)
19. R.E. Banfield, L.O. Hall, K.W. Bowyer, and W.P. Kegelmeyer Ensemble diversity measures and their application to thinning Information Fusion 6 1 2005 49 62
20. G. Rogova Combining the results of several neural network classifiers Neural Networks 7 5 1994 777 781
21. J. Meynet, and J.-P. Thiran Information theoretic combination of pattern classifiers Pattern Recognition 43 2010 3412 3421
22. I. Partalas, G. Tsoumakas, and I. Vlahavas Pruning an ensemble of classifiers via reinforcement learning Neurocomputing 72 79 2009 1900 1909
23. Y. Zhang, S. Burer, and W.N. Street Ensemble pruning via semi-definite programming Journal of Machine Learning Research 7 2006 1315 1338 (Pubitemid 44024598)
24. L.I. Kuncheva Clustering-and-selection model for classifier combination Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies 2000 185 188
25. R. Liu, and B. Yuan Multiple classifiers combination by clustering and selection Information Fusion 2 3 2001 163 168 (Pubitemid 33396498)
26. L.A. Rastrigin, R.H. Erenstein, Method of collective recognition, Energoizdat, Moscow, 1981 (in Russian).
27. K. Woods, W.P. Kegelmeyer Jr., and K. Bowyer Combination of multiple classifiers using local accuracy estimates IEEE Transactions on Pattern Analysis and Machine Intelligence 19 4 1997 405 410 (Pubitemid 127762896)
28. P.C. Smits Multiple classifier systems for supervised remote sensing image classification based on dynamics classifier selection IEEE Transactions on Geoscience and Remote Sensing 40 4 2002 801 813 (Pubitemid 34741654)
29. G. Giacinto, and F. Roli Dynamic classifier selection based on multiple classifier behaviour Pattern Recognition 34 2001 1879 1881
30. A.H.R. Ko, R. Sabourin, and A.S. Britto Jr. From dynamic classifier selection to dynamic ensemble selection Pattern Recognition 41 5 2008 1718 1731
31. E.M.D. Santos, R. Sabourin, and P. Maupin A dynamic overproduce-and- choose strategy for the selection of classifier ensembles Pattern Recognition 41 10 2008 2993 3009
32. R.A. Jacobs, M.I. Jordan, S.J. Nowlan, and G.E. Hinton Adaptive mixtures of local experts Neural Computation 3 1991 79 87
33. G. Giacinto, and F. Roli A theoretical framework for dynamic classifier selection Fifteenth International Conference on Pattern Recognition 2000 8 11
34. T. Woloszynski, and M. Kurzynski On a new measure of classifier competence applied to the design of multiclassifier systems Image Analysis and Processing - ICIAP 2009, Lecture Notes in Computer Science 2009 995 1004
35. T. Woloszynski, and M. Kurzynski On a new measure of classifier competence in the feature space Computer Recognition Systems 3, Advances in Soft Computing 2009 285 292
36. T. Woloszynski, and M. Kurzynski A measure of competence based on randomized reference classifier for dynamic ensemble selection Twentieth International Conference on Pattern Recognition 2010 4194 4197
37. A. Asuncion, D.J. Newman, UCI machine learning repository URL 〈 http://www.ics.uci.edu/~mlearn/MLRepository.html 〉, 2007.
38. L.I. Kuncheva, Ludmila kuncheva collection URL 〈 http://www.bangor.ac.uk/~mas00a/activities/patrec1.html 〉, 2004.
39. E.E. project: ROARS, Phoneme database from elena project, URL 〈 http://www.dice.ucl.ac.be/neuralnets/Research/Projects/ELENA/ 〉.
40. R.O. Duda, P.E. Hart, and D.G. Stork Pattern Classification 2001 Wiley-Interscience
41. J.O. Berger Statistical Decision Theory and Bayesian Analysis 1985 Springer Verlag New York
42. H.A. David, and H.N. Nagaraja Order Statistics 2003 Wiley-Interscience
43. T. Woloszynski, Classifier competence based on probabilistic modeling (ccprmod.m) at Matlab central file exchange URL 〈 http://www.mathworks.com/ matlabcentral/fileexchange/28391-classifier-competence-based-on-probabilistic- modeling 〉, 2010.
44. W. Meisel Potential functions in mathematical pattern recognition IEEE Transactions on Computers C-18 1969 911 918
45. C.L. Lawson, and R.J. Hanson Solving Least Squares Problems 1987 Society for Industrial Mathematics
46. E. Hullermeier, and S. Vanderlooy Combining predictions in pairwise classification: an optimal adaptive voting strategy and its relation to weighted voting Pattern Recognition 43 2010 128 142
47. R.P. Duin, P. Juszczak, P. Paclik, E. Pekalska, D. de Ridder, D. Tax, S. Verzakov, PR-Tools4.1, a matlab toolbox for pattern recognition, 2007.
48. D.H. Wolpert Stacked generalization Neural Networks 5 2 1992 241 260
49. I.-C. Yeh, K.-J. Yang, and T.-M. Ting Knowledge discovery on RFM model using Bernoulli sequence Expert Systems with Applications 36 3 2009 5866 5871
50. W.H. Wolberg, and O.L. Mangasarian Multisurface method of pattern separation for medical diagnosis applied to breast cytology Proceedings of the National Academy of Sciences, U.S.A. 87 1990 9193 9196 (Pubitemid 120030591)
51. R. King, A. Karwath, A. Clare, and L. Dehaspe The utility of different representations of protein sequence for predicting functional class Bioinformatics 17 5 2001 445 454 (Pubitemid 32440305)
52. M.A. Little, P.E. McSharry, D.A.E. Costello, I. Moroz, Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection, Biomedical Engineering Online 6 (23) (2007).
53. M.I. Jordan, and R.A. Jacobs Hierarchical mixtures of experts and the EM algorithm Neural Computation 6 2 1994 181 214
54. S.R. Waterhouse, and A.J. Robinson Classification using hierarchical mixtures of experts Neural Networks for Signal Processing [1994] IV, Proceedings of the 1994 IEEE Workshop 1994 177 186
55. J. Demsar Statistical comparison of classifiers over multiple data sets Journal of Machine Learning Research 7 2006 1 30 (Pubitemid 43022939)