Credal-C4.5: Decision tree based on imprecise probabilities to classify noisy data

Expert Systems with Applications

Volumn 41, Issue 10, 2014, Pages 4625-4637

  Mantas, Carlos J ^a   Abellán, Joaquín ^a  

^a UNIVERSITY OF GRANADA   (Spain)

Author keywords

C4.5 algorithm; Credal decision trees; Imprecise Dirichlet Model; Imprecise probabilities; Noisy data; Uncertainty measures

Indexed keywords

C4.5 ALGORITHM; IMPRECISE DIRICHLET MODEL; IMPRECISE PROBABILITIES; NOISY DATA; UNCERTAINTY MEASURES;

ALGORITHMS; PROBABILITY DISTRIBUTIONS; SET THEORY;

DECISION TREES;

EID: 84896857348     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2014.01.017     Document Type: Article

References (28)

1. J. Abellán, and S. Moral Building classification trees using the total uncertainty criterion International Journal of Intelligent Systems 18 12 2003 1215 1225
2. J. Abellán, and S. Moral Upper entropy of credal sets. Applications to credal classification International Journal of Approximate Reasoning 39 2-3 2005 235 255
3. J. Abellán Uncertainty measures on probability intervals from Imprecise Dirichlet model International Journal of General Systems 35 5 2006 509 528
4. J. Abellán, and S. Moral An algorithm that computes the upper entropy for order-2 capacities International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 14 2 2006 141 154
5. J. Abellán, G.J. Klir, and S. Moral Disaggregated total uncertainty measure for credal sets International Journal of General Systems 35 1 2006 29 44
6. J. Abellán, and A. Masegosa Requirements for total uncertainty measures in Dempster-Shafer theory of evidence International Journal of General Systems 37 6 2008 733 747
7. J. Abellán, and A. Masegosa A filter-wrapper method to select variables for the Naive Bayes classifier based on credal decision trees International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 17 6 2009 833 854
8. J. Abellán, and A.R. Masegosa An experimental study about simple decision trees for Bagging ensemble on data sets with classification noise C. Sossai, G. Chemello, ECSQARU LNCS Vol. 5590 2009 Springer 446 456
9. J. Abellán, and A. Masegosa Bagging schemes on the presence of noise in classification Expert Systems with Applications 39 8 2012 6827 6837
10. J. Abellán Ensembles of decision trees based on imprecise probabilities and uncertainty measures Information Fusion 14 4 2013 423 430
11. J. Abellán An application of non-parametric predictive inference on multi-class classification high-level-noise problems Expert Systems with Applications 40 2013 4585 4592
12. J. Abellán, R.M. Baker, F.P.A. Coolen, R. Crossman, and A. Masegosa Classification with decision trees from a nonparametric predictive inference perspective Computational Statistics and Data Analysis 71 2014 789 802
13. J. Abellán, and C.J. Mantas Improving experimental studies about ensembles of classifers for bankruptcy prediction and credit scoring Expert Systems with Applications 41 2014 3825 3830
14. J. Demsar Statistical comparison of classifiers over multiple data sets Journal of Machine Learning Research 7 2006 1 30
15. U.M. Fayyad, and K.B. Irani Multi-valued interval discretization of continuous-valued attributes for classification learning Proceedings of the 13th international joint conference on artificial intelligence 1993 Morgan Kaufman San Mateo 1022 1027
16. Frenay, B., & Verleysen, M. (in press). Classification in the presence of label noise: A survey. IEEE Transactions on Neural Networks and Learning Systems. .
17. M. Friedman A comparison of alternative tests of significance for the problem of m rankings Annals of Mathematical Statistics 11 1940 86 92
18. J. Alcalá-Fdez, L. Sánchez, S. García, M.J. Del Jesus, S. Ventura, and J.M. Garrell et al. KEEL: A software tool to assess evolutionary algorithms to data mining problems Soft Computing 13 3 2009 307 318
19. G.J. Klir Uncertainty and information Foundations of generalized information theory 2006 John Wiley Hoboken, NJ
20. C.J. Mantas, and J. Abellán Analysis and extension of decision trees based on imprecise probabilities: Application on noisy data Expert Systems with Applications 41 2014 2514 2525
21. Nemenyi, P. B. (1963). Distribution-free multiple comparison (Ph.D. thesis). Princenton University.
22. J.R. Quinlan Induction of decision trees Machine Learning 1 1986 81 106
23. Quinlan, J. R. (1999). Programs for machine learning. Morgan Kaufmann series in machine learning.
24. Rokach, L., & Maimon, O. (2010). Classification trees. Data mining and knowledge discovery handbook (pp. 149-174).
25. P. Walley Inferences from multinomial data, learning about a bag of marbles Journal of the Royal Statistical Society, Series B 58 1996 3 57
26. Y. Wang Imprecise probabilities based on generalised intervals for system reliability assessment International Journal of Reliability and Safety 4 30 2010 319 342
27. I.H. Witten, and E. Frank Data mining, practical machine learning tools and techniques 2nd edition. 2005 Morgan Kaufman San Francisco
28. K. Weichselberger The theory of interval-probability as a unifying concept for uncertainty International Journal of Approximate Reasoning 24 2-3 2000 149 170