Online active learning of decision trees with evidential data

Pattern Recognition

Volumn 52, Issue , 2016, Pages 33-45

  Ma, Liyao ^a   Destercke, Sébastien ^b   Wang, Yong ^a  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b UNIVERSITÉ DE TECHNOLOGIE DE COMPIÈGNE   (France)

Author keywords

Active learning; Belief functions; Decision tree; Evidential likelihood; Uncertain data

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING; DECISION TREES; UNCERTAINTY ANALYSIS;

ACTIVE LEARNING; BELIEF FUNCTION; DECISION TREE LEARNING; EPISTEMIC UNCERTAINTIES; EVIDENTIAL LIKELIHOOD; LEARNING PROCEDURES; UNCERTAIN DATAS;

TREES (MATHEMATICS);

EID: 84951756554     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2015.10.014     Document Type: Article

References (34)

1. J. Josse, M. Chavent, B. Liquet, and F. Husson Handling missing values with regularized iterative multiple correspondence analysis J. Classif. 29 1 2012 91 116 10.1007/s00357-012-9097-0
2. J.R. Quinlan, Decision trees as probabilistic classifiers, in: Proceedings of the Fourth International Workshop on Machine Learning, 1987, pp. 31-37. http://dx.doi.org/10.1016/b978-0-934613-41-5.50007-6.
3. S. Tsang, B. Kao, K.Y. Yip, W.-S. Ho, and S.D. Lee Decision trees for uncertain data IEEE Trans. Knowl. Data Eng. 23 1 2011 64 78 10.1109/tkde.2009.175
4. Y. Yuan, and M.J. Shaw Induction of fuzzy decision trees Fuzzy Sets Syst. 69 2 1995 125 139 10.1016/0165-0114(94)00229-z
5. X. Wang, X. Liu, W. Pedrycz, and L. Zhang Fuzzy rule based decision trees Pattern Recognit. 48 1 2015 50 59 10.1016/j.patcog.2014.08.001
6. B. Qin, Y. Xia, F. Li, Dtu: a decision tree for uncertain data, in: Advances in Knowledge Discovery and Data Mining, Springer, Berlin, Heidelberg, 2009, pp. 4-15.
7. C. Liang, Y. Zhang, Q. Song, Decision tree for dynamic and uncertain data streams, in: ACML, 2010, pp. 209-224.
8. C.C. Aggarwal, and P.S. Yu A survey of uncertain data algorithms and applications IEEE Trans. Knowl. Data Eng. 21 5 2009 609 623 10.1109/tkde.2008.190
9. M. Mohri, Learning from uncertain data, in: Learning Theory and Kernel Machines, Springer, Berlin, Heidelberg, 2003, pp. 656-670.
10. E. Huellermeier Learning from imprecise and fuzzy observations: data disambiguation through generalized loss minimization Int. J. Approx. Reason. 55 7 2014 1519 1534 10.1016/j.ijar.2013.09.003
11. L. Liu, T. Dietterich, Learnability of the superset label learning problem, in: Proceedings of the 31st International Conference on Machine Learning (ICML-14), 2014, pp. 1629-1637.
12. I. Couso, and L. Sánchez Harnessing the information contained in low-quality data sources Int. J. Approx. Reason. 55 7 2014 1485 1486 10.1016/j.ijar.2014.05.006
13. A.P. Dempster Upper and lower probabilities induced by a multivalued mapping Ann. Math. Stat. 1967 325 339 10.1214/aoms/1177698950
14. G. Shafer, et al., A Mathematical Theory of Evidence, vol. 1, Princeton University Press, Princeton, 1976. http://dx.doi.org/10.2307/1268172.
15. Z. Elouedi, K. Mellouli, and P. Smets Belief decision trees: theoretical foundations Int. J. Approx. Reason. 28 2 2001 91 124 10.1016/s0888-613x(01)00045-7
16. S. Trabelsi, Z. Elouedi, and K. Mellouli Pruning belief decision tree methods in averaging and conjunctive approaches Int. J. Approx. Reason. 46 3 2007 568 595 10.1016/j.ijar.2007.02.004
17. P. Vannoorenberghe, T. Denoeux, Handling uncertain labels in multiclass problems using belief decision trees, in: Proceedings of IPMU, vol. 3, 2002, pp. 1919-1926.
18. P. Vannoorenberghe On aggregating belief decision trees Inf. Fusion 5 3 2004 179 188 10.1016/j.inffus.2004.01.001
19. N. Sutton-Charani, S. Destercke, T. Denœux, Learning decision trees from uncertain data with an evidential EM approach, in: 12th International Conference on Machine Learning and Applications (ICMLA), vol. 1, IEEE, Miami, Florida, USA, 2013, pp. 111-116. http://dx.doi.org/10.1109/icmla.2013.26.
20. N. Sutton-Charani, S. Destercke, T. Denœux, Training and evaluating classifiers from evidential data: application to e 2 m decision tree pruning, in: Belief Functions: Theory and Applications, Springer International Publishing, Berlin, Heidelberg, 2014, pp. 87-94. http://dx.doi.org/10.1007/978-3-319-11191-9-10.
21. T. Denœux, Maximum likelihood from evidential data: an extension of the EM algorithm, in: Combining Soft Computing and Statistical Methods in Data Analysis, Springer, Berlin, Heidelberg, 2010, pp. 181-188. http://dx.doi.org/10.1007/978-3-642-14746-3-23.
22. B. Settles, Active learning literature survey, Computer Sciences Technical Report 1648, University of Wisconsin-Madison, 2009.
23. A. Bordes, S. Ertekin, J. Weston, and L. Bottou Fast kernel classifiers with online and active learning J. Mach. Learn. Res. 6 2005 1579 1619
24. T. Denoeux Likelihood-based belief function: justification and some extensions to low-quality data Int. J. Approx. Reason. 55 7 2014 1535 1547 10.1016/j.ijar.2013.06.007
25. J.R. Quinlan Induction of decision trees Mach. Learn. 1 1 1986 81 106 10.1007/bf00116251
26. S.B. Kotsiantis Decision trees: a recent overview Artif. Intell. Rev. 39 4 2013 261 283
27. J.R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, Inc., San Francisco, 1993.
28. L. Breiman, J. Friedman, C.J. Stone, and R.A. Olshen Classification and Regression Trees 1984 CRC Press New York
29. T. Denoeux Maximum likelihood estimation from uncertain data in the belief function framework IEEE Trans. Knowl. Data Eng. 25 1 2013 119 130 10.1109/tkde.2011.201
30. A. Birnbaum On the foundations of statistical inference J. Am. Stat. Assoc. 57 298 1962 269 306
31. T. Reineking, K. Schill, Evidential object recognition based on information gain maximization, in: Belief Functions: Theory and Applications, Springer International Publishing, Berlin, Heidelberg, 2014, pp. 227-236.
32. P. Walley, Statistical Reasoning with Imprecise Probabilities, Monographs on Statistics and Applied Probability, Chapman and Hall/CRC, London, 1991. (https://books.google.fr/books?id=-hbvAAAAMAAJ).
33. M. Lichman, UCI machine learning repository, University of California, Irvine, School of Information and Computer Sciences, 2013. (http://archive.ics.uci.edu/ml).
34. L. Liu, T.G. Dietterich, A conditional multinomial mixture model for superset label learning, in: Advances in Neural Information Processing Systems, 2012, pp. 557-565.