Ensemble-based noise detection: Noise ranking and visual performance evaluation

Data Mining and Knowledge Discovery

Volumn 28, Issue 2, 2014, Pages 265-303

  Sluban, Borut ^a   Gamberger, Dragan ^b   Lavrač, Nada ^a  

^a JO EF STEFAN INSTITUTE   (Slovenia)

^b RUDER BO KOVI INSTITUTE   (Croatia)

Author keywords

Ensembles; Noise detection; Noise ranking; Precision recall evaluation

Indexed keywords

DATA PREPROCESSING; ENSEMBLES; NOISE DETECTION; NOISE RANKING; OUTLIER IDENTIFICATION; PRECISION-RECALL EVALUATION; RANKING METHODOLOGIES; STANDARD EVALUATIONS;

STATISTICS; WEB SERVICES; WEBSITES;

SIGNAL DETECTION;

EID: 84893677824     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10618-012-0299-1     Document Type: Article

References (40)

1. Aggarwal CC, Yu PS (2001) Outlier detection for high dimensional data. In: Proceedings of the 2001 ACM SIGMOD international conference on management of data, pp 37-46
2. Brodley CE, Friedl MA (1999) Identifying mislabeled training data. J Artif Intell Res 11:131-167
3. Deanfield J, Shea M, Ribiero P, de Landsheere C, Wilson R, Horlock P, Selwyn A (1984) Transient st-segment depression as a marker of myocardial ischemia during daily life. Am J Cardiol 54(10):1195-1200
4. Demšar J, Zupan B, Leban G, Curk T (2004) Orange: From experimental machine learning to interactive data mining. In: Boulicaut JF, Esposito F, Giannotti F, Pedreschi D (eds) Knowledge discovery in databases: PKDD 2004, lecture notes in computer science. Springer, vol 3202, pp 537-539
5. Frank A, Asuncion A (2010) UCI machine learning repository. URL http://archive.ics.uci.edu/ml
6. Fürnkranz J (1997) Pruning algorithms for rule learning. Mach Learn 27:139-171
7. Gamberger D, Lavrač N (1997) Conditions for Occam's razor applicability and noise elimination. In: Lecture notes in artificial intelligence: machine learning: ECML-97, vol 1224, pp 108-123
8. Gamberger D, Lavrač N (2002) Expert-guided subgroup discovery: methodology and application. J Artif Intell Res 17:501-527
9. Gamberger D, Lavrač N, Grošelj C (1999) Experiments with noise filtering in a medical domain. In: Proceedings of 16th international conference on machine learning - ICML, Morgan Kaufmann, pp 143-151
10. Gamberger D, Lavrač N, Džeroski S (2000) Noise detection and elimination in data preprocessing: experiments in medical domains. Appl Artif Intell 14(2):205-223
11. Gamberger D, Lavrač N, Krstačić G (2003) Active subgroup mining: a case study in a coronary heart disease risk group detection. Artif Intell Med 28:27-57
12. Gelfand S, Ravishankar C, Delp E (1991) An iterative growing and pruning algorithm for classification tree design. IEEE Trans Pattern Anal Mach Intell 13:163-174
13. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The weka data mining software: an update. SIGKDD Explor Newsl 11(1):10-18
14. Hodge V, Austin J (2004) A survey of outlier detection methodologies. Artif Intell Rev 22(2):85-126
15. Khoshgoftaar TM, Rebours P (2004) Generating multiple noise elimination filters with the ensemble-partitioning filter. In: Proceedings of the 2004 IEEE International Conference on information reuse and integration. IEEE Systems, Man, and Cybernetics Society, pp 369-375.
16. Khoshgoftaar T, Seliya N, Gao K (2004) Rule-based noise detection for software measurement data. In: Proceedings of the 2004 IEEE international conference on information reuse and integration, 2004 (IRI 2004), pp 302-307
17. Khoshgoftaar TM, Zhong S, Joshi V (2005) Enhancing software quality estimation using ensemble-classifier based noise filtering. Intell Data Anal 9(1):3-27
18. Khoshgoftaar TM, Joshi VH, Seliya N (2006) Detecting noisy instances with the ensemble filter: a study in software quality estimation. Int J Softw Eng Knowl Eng 16(1):53-76
19. Kranjc J, Podpečan V, Lavrač N (2012) Clowdflows: a cloud based scientific workflow platform. In: Flach P, Bie T, Cristianini N (eds) Machine learning and knowledge discovery in databases, lecture notes in computer science. Springer, Berlin, vol 7524, pp 816-819
20. Libralon GL, Carvalho ACPLF, Lorena AC (2009) Ensembles of pre-processing techniques for noise detection in gene expression data. In: Proceedings of the 15th international conference on advances in neuro-information processing - volume part I, ICONIP'08. Springer, Berlin, pp 486-493
21. Manning CD, Raghavan P, Schtze H (2008) Introduction to Information Retrieval. Cambridge University Press, New York
22. Maron D, Ridker P, Pearson A (1998) Risk factors and the prevention of coronary heart disease. In: Wayne A, Schlant R, Fuster V (eds) HURST'S: the Heart, pp 1175-1195
23. Mingers J (1989) An empirical comparison of pruning methods for decision tree induction. Mach Learn 4:227-243
24. Miranda A, Garcia L, Carvalho A, Lorena A (2009) Use of classification algorithms in noise detection and elimination. In: Hybrid artificial intelligence systems, lecture notes in computer science. Springer, Berlin, vol 5572, pp 417-424
25. Niblett T, Bratko I (1987) Learning decision rules in noisy domains. In: Bramer M (ed) Research and development in expert systems. Cambridge University Press, Cambridge
26. Pollak S (2009) Text classification of articles on kenyan elections. In: Proceedings of the 4th language & technology conference: human language technologies as a challenge for computer science and linguistics, pp 229-233.
27. Pollak S, Coesemans R, Daelemans W, Lavrač N (2011) Detecting contrast patterns in newspaper articles by combining discourse analysis and text mining. Pragmatics 21(4):674-683
28. Quinlan JR (1987) Simplifying decision trees. Int J Man-Mach Stud 27:221-234
29. Sluban B, Gamberger D, Lavrač N (2010) Advances in class noise detection. In: Coelho H, Studer R, Wooldridge M (eds) Proceedings of the 19th European conference on artificial intelligence (ECAI 2010), pp 1105-1106
30. Sluban B, Gamberger D, Lavrač N (2011) Performance analysis of class noise detection algorithms. In: Ågotnes T (ed) STAIRS 2010 - proceedings of the fifth starting AI researchers' symposium, pp 303-314
31. Teng CM (1999) Correcting noisy data. In: Proceedings of the sixteenth international conference on machine learning, pp 239-248
32. Van Hulse JD, Khoshgoftaar TM (2006) Class noise detection using frequent itemsets. Intell Data Anal 10(6):487-507
33. Van Hulse JD, Khoshgoftaar TM, Huang H (2007) The pairwise attribute noise detection algorithm. Knowl Inf Syst 11(2):171-190
34. Verbaeten S (2002) Identifying mislabeled training examples in ilp classification problems. In: Proceedings of twelfth Belgian-Dutch conference on machine learning, pp 1-8
35. Verbaeten S, Van Assche A (2003) Ensemble methods for noise elimination in classification problems. In: Windeatt T, Roli F (eds) Multiple classifier systems, lecture notes in computer science. Springer, Berlin, vol 2709, pp 317-325
36. Wiswedel B, Berthold MR (2005) Fuzzy clustering in parallel universes with noise detection. In: Proceedings of the ICDM 2005 workshop on computational intelligence in data mining, pp 29-37
37. Yin H, Dong H, Li Y (2009) A cluster-based noise detection algorithm. International workshop on database technology and applications, pp 386-389
38. Zhong S, Tang W, Khoshgoftaar TM (2005) Boosted noise filters for identifying mislabeled data. Technical report, Department of computer science and engineering, Florida Atlantic University
39. Zhu X, Wu X (2004) Class noise vs. attribute noise: a quantitative study of their impacts. Artif Intell Rev 22:177-210
40. Zhu X, Wu X, Chen Q (2003) Eliminating class noise in large datasets. In: Proceedings of the international conference on machine learning, pp 920-927