AdaptativeCC4.5: Credal C4.5 with a rough class noise estimator

Expert Systems with Applications

Volumn 92, Issue , 2018, Pages 363-379

  Abellán, Joaquín ^a   Mantas, Carlos J ^a   Castellano, Javier G ^a  

^a UNIVERSITY OF GRANADA   (Spain)

Author keywords

Classification; Decision support system; Decision tree; Imprecise probabilities; Noisy data; Uncertainty measures

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); DECISION MAKING; DECISION SUPPORT SYSTEMS; DECISION TREES; EQUIVALENCE CLASSES; PROBABILITY; TREES (MATHEMATICS);

DEGREE OF NOISE; DIRECT METHOD; IMPRECISE PROBABILITIES; KNOWLEDGE EXTRACTION; NOISE ESTIMATION; NOISY DATA; TRAINING DATA; UNCERTAINTY MEASURES;

DATA MINING;

EID: 85030540807     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2017.09.057     Document Type: Article

References (49)

1. Abellán, J., Uncertainty measures on probability intervals from the imprecise dirichlet model. International Journal of General Systems 35:5 (2006), 509–528, 10.1080/03081070600687643.
2. Abellán, J., Baker, R.M., Coolen, F.P.A., Maximising entropy on the nonparametric predictive inference model for multinomial data. European Journal of Operational Research 212:1 (2011), 112–122, 10.1016/j.ejor.2011.01.020.
3. Abellán, J., Mantas, C.J., Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring. Expert Systems with Applications 41:8 (2014), 3825–3830, 10.1016/j.eswa.2013.12.003.
4. Abellán, J., Masegosa, A., An experimental study about simple decision trees for bagging ensemble on datasets with classification noise. Sossai, C., Chemello, G., (eds.) Symbolic and quantitative approaches to reasoning with uncertainty Lecture Notes in Computer Science, 5590, 2009, Springer Berlin Heidelberg, 446–456, 10.1007/978-3-642-02906-6_39.
5. Abellán, J., Masegosa, A.R., Bagging schemes on the presence of class noise in classification. Expert Systems with Applications 39:8 (2012), 6827–6837, 10.1016/j.eswa.2012.01.013.
6. Abellán, J., Moral, S., Building classification trees using the total uncertainty criterion. International Journal of Intelligent Systems 18:12 (2003), 1215–1225, 10.1002/int.10143.
7. Abellán, J., Moral, S., An algorithm to compute the upper entropy for order-2 capacieties. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 14:02 (2006), 141–154, 10.1142/S0218488506003911.
8. Aha, D.W., Kibler, D., Albert, M.K., Instance-based learning algorithms. Machine Learning 6:1 (1991), 37–66, 10.1007/BF00153759.
9. Alcalá-Fdez, J., Sánchez, L., García, S., del Jesus, M., Ventura, S., Garrell, J., et al. Keel: A software tool to assess evolutionary algorithms for data mining problems. Soft Computing 13:3 (2009), 307–318, 10.1007/s00500-008-0323-y.
10. Chaoqun, L., Victor, S., Liangxiao, J., Hongwei, L., Noise filtering to improve data and model quality for crowdsourcing. Knowledge Based Systems 107 (2016), 96–103, 10.1016/j.knosys.2016.06.003.
11. Delany, S., Segata, N., MacNamee, B., Profiling instances in noise reduction. Knowledge Based Systems 31 (2012), 28–40, 10.1016/j.knosys.2012.01.015.
12. Demšar, J., Statistical comparisons of classifiers over multiple data sets. Journalof Machine Learning Research 7 (2006), 1–30.
13. Everitt, B.S., Landau, S., Leese, M., Stahl, D., Miscellaneous clustering methods. 2011, John Wiley And Sons, Ltd, 215–255).
14. Fix, E., Hodges, J.L., Discriminatory analysis. nonparametric discrimination: Consistency properties. International Statistical Review / Revue Internationale de Statistique 57:3 (1989), 238–247, 10.2307/1403797.
15. Frenay, B., Verleysen, M., Classification in the presence of label noise: A survey. Neural Networks and Learning Systems, IEEE Transactions on 25:5 (2014), 845–869, 10.1109/TNNLS.2013.2292894.
16. Friedman, M., The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association 32 (1937), 675–701.
17. Friedman, M., A comparison of alternative tests of significance for the problem of m rankings. The Annals of Mathematical Statistics 11:1 (1940), 86–92, 10.1214/aoms/1177731944.
18. Garcia, L.P., Sáez, J.A., Luengo, J., Lorena, A.C., de Carvalho, A.C., Herrera, F., Using the one-vs-one decomposition to improve the performance of class noise filters via an aggregation strategy in multi-class classification problems. Knowledge Based Systems 90 (2015), 153–164, 10.1016/j.knosys.2015.09.023.
19. García, S., Luengo, J., Herrera, F., Dealing with noisy data. Data preprocessing in data mining, 2015, Springer International Publishing, 107–145) 10.1007/978-3-319-10247-4_5.
20. García, V., Mollineda, R.A., Sánchez, J.S., On the k-nn performance in a challenging scenario of imbalance and overlapping. Pattern Analysis and Applications 11:3 (2008), 269–280, 10.1007/s10044-007-0087-5.
21. Geman, S., Bienenstock, E., Doursat, R., Neural networks and the bias/variance dilemma. Neural Computation(4), 1992, 1–58, 10.1162/neco.1992.4.1.1.
22. Hand, D.J., Construction and assessment of classification rules. 1997, John Wiley and Sons, New York.
23. Hunt, E., Marin, J., Stone, P., Experiments in induction. 1966, Academic Press.
24. James, G., Witten, D., Hastie, T., Tibshirani, R., An introduction to statistical learning. Springer Texts in Statistics, 103, 2013, Springer, 10.1007/978-1-4614-7138-7.
25. Jo, T., Japkowicz, N., Class imbalances versus small disjuncts. ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets 6:1 (2004), 40–49, 10.1145/1007730.1007737.
26. Klir, G.J., Uncertainty and information: Foundations of generalized information theory. 2005, John Wiley And Sons, Inc., 10.1002/0471755575.
27. Kohavi, R., Wolpert, D., Bias plus variance decomposition for zero-one loss functions. Proceedings of the Thirteenth International Conference of Machine Learning, 1996, 275–283.
28. Kononenko, I., Kukar, M., Machine learning and data mining: Introduction to principles and algorithms. 2007, Woodhead Publishing Limited.
29. Lichman, M. (2013). UCI machine learning repository. http://archive.ics.uci.edu/ml.
30. Liu, T., Tao, D., Classification with noisy labels by importance reweighting. IEEE Transactions on Pattern Analysis and Machine Intelligence 38 (2016), 447–461.
31. Maimon, O., Rokach, L., Data mining and knowledge discovery handbook. 2005, Springer-Verlag New York, NJ, USA, 10.1007/978-0-387-09823-4.
32. Mantas, C.J., Abellán, J., Analysis and extension of decision trees based on imprecise probabilities: Application on noisy data. Expert Systems with Applications 41:5 (2014), 2514–2525, 10.1016/j.eswa.2013.09.050.
33. Mantas, C.J., Abellán, J., Credal-c4.5: Decision tree based on imprecise probabilities to classify noisy data. Expert Systems with Applications 41:10 (2014), 4625–4637, 10.1016/j.eswa.2014.01.017.
34. Mantas, C.J., Abellán, J., Castellano, J.G., Analysis of credal-c4.5 for classification in noisy domains. Expert Systems with Applications 61 (2016), 314–326, 10.1016/j.eswa.2016.05.035.
35. Natarajan, N., Dhillon, I.S., Ravikumar, P., Tewari, A., Learning with noisy labels. Proceedings of the 26th international conference on neural information processing systems NIPS′13, 2013, Curran Associates Inc., USA, 1196–1204.
36. Nemenyi, P., Distribution-free multiple comparisons, 1963, Princeton University, New Jersey, USA Doctoral dissertation.
37. Olvera-López, J.A., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Kittler, J., A review of instance selection methods. Artificial Intelligence Review 34:2 (2010), 133–143, 10.1007/s10462-010-9165-y.
38. Quinlan, J.R., Induction of decision trees. Machine Learning 1:1 (1986), 81–106, 10.1023/A:1022643204877.
39. Quinlan, J.R., C4.5: Programs for machine learning. 1993, Morgan Kaufmann Publishers Inc, San Francisco, CA, USA.
40. Rokach, L., Maimon, O., Data mining with decision trees: Theory and applications. Machine Perception and Artificial Intelligence, 81, 2008, World Scientific.
41. Sáez, J.A., Luengo, J., Herrera, F., Fuzzy rule based classification systems versus crisp robust learners trained in presence of class noise's effects: A case of study. Intelligent systems design and applications (isda), 2011 11th international conference on, 2011, 1229–1234, 10.1109/ISDA.2011.6121827.
42. Sáez, J.A., Luengo, J., Herrera, F., Evaluating the classifier behavior with noisy data considering performance and robustness: The equalized loss of accuracy measure. Neurocomputing 176 (2014), 26–35, 10.1016/j.neucom.2014.11.086 Recent Advancements in Hybrid Artificial Intelligence Systems and its Application to Real-World Problems, selected papers from the {HAIS} 2013 conference.
43. Sammut, C., Webb, G.I., Sammut, C., Webb, G.I., (eds.) Encyclopedia of machine learning, 2011, Springer, 100–101, 10.1007/978-0-387-30164-8.
44. Shannon, C.E., A mathematical theory of communication. Bell System Technical Journal 27:3 (1948), 379–423, 10.1002/j.1538-7305.1948.tb01338.x.
45. Walley, P., Inferences from multinomial data; learning about a bag of marbles (with discussion). Journal of the Royal Statistical Society. Series B (Methodological) 58:1 (1996), 3–57, 10.2307/2346164.
46. Wilcoxon, F., Individual comparisons by ranking methods. Biometrics Bulletin 1:6 (1945), 80–83, 10.2307/3001968.
47. Witten, I.H., Frank, E., Data mining: Practical machine learning tools and techniques. 2nd Morgan Kaufmann Series in Data Management Systems, 2005, Morgan Kaufmann Publishers Inc, San Francisco, CA, USA.
48. Wu, Y., Ianakiev, K., Govindaraju, V., Improved k-nearest neighbor classification. Pattern Recognition 35:10 (2002), 2311–2318, 10.1016/S0031-3203(01)00132-7.
49. Yang, T., Mahdavi, M., Jin, R., Zhang, L., Zhou, Y., Multiple kernel learning from noisy labels by stochastic programming. 29th International Conference on Machine Learning, ICML 2012, 2012, Edinburgh, United Kingdom.