Modifications of the construction and voting mechanisms of the Random Forests Algorithm

Data and Knowledge Engineering

Volumn 87, Issue , 2013, Pages 41-65

  Tripoliti, Evanthia E ^a   Fotiadis, Dimitrios I ^a   Manis, George ^a  

^a UNIVERSITY OF IOANNINA   (Greece)

Author keywords

Classification; Decision tree; Ensemble methods; Random Forests; Weighted voting

Indexed keywords

CLASSIFICATION (OF INFORMATION); INFORMATION SYSTEMS; KNOWLEDGE ENGINEERING;

ENSEMBLE METHODS; EVALUATION MEASURES; NEAREST NEIGHBORS; OPTIMIZATION TECHNIQUES; PREDICTION PERFORMANCE; RANDOM FORESTS; VOTING MECHANISM; WEIGHTED VOTING;

DECISION TREES;

EID: 84884977430     PISSN: 0169023X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.datak.2013.07.002     Document Type: Article

References (42)

1. T.G. Dietterich Ensemble methods in machine learning Lecture Notes in Computer Science 1857 2000 1 15
2. L.I. Kuncheva Combining Pattern Classifiers 2004 John Wiley and Sons Methods and Algorithms
3. N.C. Oza, R. Polikar, J. Kittler, and F. Roli Proceedings of the Sixth International Workshop on Multiple Classifier Systems, MCS 2005, Seaside, CA, USA 2005
4. L. Breiman Bagging predictors Machine Learning 24 2 1996 123 140
5. Y. Freud, 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
6. T.K. Ho The random subspace method for constructing decision forests IEEE Transactions on Pattern Analysis and Machine Intelligence 20 8 1998 832 844
7. L. Breiman Random forests Machine Learning 45 2001 5 32
8. M. Robnik-Sikonja Improving Random Forests 2004 Proceedings of the European Conference on Machine Learning pp. 359-369
9. S. Bernard, L. Heutte, and S. Adam Forest-RK: a new random forest induction method D.S. Huang, ICIC 2008, LNAI 5227 2008 Springer Heidelberg 430 437
10. J.J. Rodriguez, L.I. Kuncheva, and C.J. Alonso Rotation Forest: a new classifier ensemble method IEEE Transaction on Pattern Analysis and Machine Learning 28 10 2006 1619 1630
11. T.D. Lemmond, B.Y. Chen, A.O. Hatch, and W.G. Hanley An extended study of the discriminant random forest Robert Stahlbock, Data Mining, 1 Special Issue in Annals of Information Systems, Annals of Information Systems 2009 SpringerLink 123 146
12. A. Tsymbal, M. Pechenizkiy, and P. Cunningham Dynamic Integration with Random Forests, Machine Learning: ECML 2006 Springer Berlin Heidelberg
13. H. Hu, J. Li, H. Wang, G. Daggard, and M. Shi A maximally diversified multiple decision tree algorithm for microarray data classification The 2006 Workshop on Intelligent Systems for Bioinformatics (WISB2006), Hobart, Australia 2006
14. S. Gunter, and H. Bunke Optimization of weights in a multiple classifier handwritten word recognition system using a genetic algorithm Electronic Letters on Computer Vision and Image Analysis 2004 25 41
15. H. Boström Estimating class probabilities in random forests Proceedings of the Sixth International Conference on Machine Learning and Applications IEEE Computer Society 2007 211 216
16. L. Jiang, C. Li, and Z. Cai Learning decision tree for ranking Knowledge and Information Systems 20 1 2009 123 135
17. L. Jiang, and C. Li An empirical study on class probability estimates in decision tree learning Journal of Software 6 7 2011 1368 1373
18. L. Jiang, and C. Li An empirical study on attribute selection measures in decision tree learning Journal of Computational Information Systems 6 1 2010 105 112
19. P. Cunningham A taxonomy of similarity mechanisms for case-based reasoning Technical Report UCD-CSI-2008-01 2008
20. D.R. Wilson, and T.R. Martinez Improved heterogeneous distance functions Journal of Artificial Intelligence Research 6 1997 1 34
21. E.E. Tripoliti, D.I. Fotiadis, M. Argyropoulou, and G. Manis A six stage approach for the diagnosis of the Alzheimer's disease based on fMRI data Journal of Biomedical Informatics 43 2010 307 320
22. S. Bernard, L. Heutte, and S. Adam On the selection of decision trees in Random Forests IEEE-ENNS International Joint Conference on Neural Networks 2009 302 307
23. G.W. Brier Verification of forecast expressed in terms of probability Monthly Weather Review 78 1950 1 3
24. E. Gatnar A diversity measure for tree-based classifier ensembles D. Baier, Data Analysis and Decision Support 2005 Springer Heidelberg 30 38
25. L.I. Kuncheva, and C.J. Whitaker Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy Journal of Machine Learning 51 2 2003 181 207
26. A. Albalate, and D. Suendermann A combination approach to cluster validation based on statistical quantiles International Joint Conference on Bioinformatics, Systems Biology and Intelligent, Computing 2009 549 555
27. R. Sharan, A. Maron-Katz, and R. Shamir CKICK and EXPANDER: a system for clustering and visualizing gene expression data Bioinformatics 19 4 2003 1787 1799
28. D. Kim, Y. Park, and D. Park Novel validity index for determination of the optimal number of clusters IEICE Transactions on Information and Systems E84-D 2001 281 285
29. A. Frank, and A. Asuncion UCI Machine Learning Repository [ http://archive.ics.uci.edu/ml ] 2010 Irvine, CA: University of California, School of Information and Computer Science
30. R.L. Iman, and J.M. Davenport Approximations of the critical region of the Friedman statistic Communications in Statistics 1980 571 595
31. D.J. Sheskin Handbook of Parametric and Nonparametric Statistical Procedures 2000 CRC Press Boca Raton
32. T.G. Dietterich Approximate statistical tests for comparing supervised classification learning algorithms Neural Computation 10 1998 1895 1924
33. J. Zar Biostatistical Analysis 5th Edition 2010 Pearson Prentice-Hall Upper Saddle River, New Jersey
34. J. Luengo, S. Garcia, and F. Herrera A study on the use of statistical tests for experimentation with neural networks F. Sandoval, IWANN 2007, LNCS 4507 2007 Springer Verlag Berlin Heidelberg 72 79
35. J. Demsar Statistical comparisons of classifiers over multiple data sets Journal of Machine Learning Research 7 2006 1 30
36. F. Janssen, and J. Furnkranz On the quest for optimal rule learning heuristics Machine Learning 2010 343 379
37. L. Mitchell, T.M. Sloan, M. Mewissen, P. Ghazal, T. Forster, M. Piotrowski, and A.S. Trew A parallel random forest classifier for R Proceedings of the Second International Workshop on Emerging Computational Methods for the Life Sciences (ECMLS '11) 2011 ACM, New York NY, USA 1 6
38. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. Witten The WEKA data mining software: an update SIGKDD Explorations 11 1 2009 10 18
39. L. Jiang Learning random forests for ranking Frontiers of Computer Science in China 5 1 2011 79 86
40. H. Fu, and G. Qiu Fast semantic image retrieval based on random forest In Proceedings of the 20th ACM International Conference on Multimedia (MM '12) 2012 ACM, New York NY, USA 909 912
41. H. Fu, and G. Qiu Random forest for image annotation A. Fitzgibbon, ECCV 2012, Part VI, LNCS 7577 2012 Springer-Verlag Berlin Heidelberg 86 89
42. S. Saha, and A. Ekbal Combining multiple classifiers using vote based classifier ensemble technique for named entity recognition Data & Knowledge Engineering 85 2013 15 39