An empirical study of the noise impact on cost-sensitive learning

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2007, Pages 1168-1173

  Zhu, Xingquan ^a,c   Wu, Xindong ^b   Khoshgoftaar, Taghi M ^a   Shi, Yong ^c  

^a Florida Atlantic Univ   (United States)

^b University of Vermont   (United States)

^c UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ACCURACY; COST-SENSITIVE CLASSIFICATION; COST-SENSITIVE LEARNING; EMPIRICAL STUDIES; IMPACT OF NOISE; INDUCTIVE LEARNING; MISCLASSIFICATION COSTS; TRAINING EXAMPLE;

ARTIFICIAL INTELLIGENCE; COSTS; DATA HANDLING; LEARNING SYSTEMS; NOISE POLLUTION;

COST BENEFIT ANALYSIS;

EID: 84880897165     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. th KDD Conference, 2004.
2. Blake C. & Merz C. (98), UCI Data Repository, 1998.
3. Bradford, J. Kunz C., Kohavi R., Brunk C., & Brodley C. (98), Pruning decision trees with misclassification costs. Proc. of the ECML Conf., 1998.
4. Brodley C. & Friedl M. (99), Identifying mislabeled training data. Journal of AI Research, 11:131-167, 1999.
5. Brefeld U., Geibel P. & Wysotzki F. (03), Support vector machines with example dependent costs. Proc. of the ECML Conf., 2003.
6. Chan P. & Stolfo S. (98), Toward scalable learning with non-uniform class and cost distributions. Proc. of the KDD Conf., 1998.
7. th KDD Conference, 1999.
8. Duda R. & Hart P. (73), Pattern classification and scene analysis, Wiley, New York, 1973.
9. Gamberger D., Lavrac N., & Groselj C. (99), Experiments with noise filtering in a medical domain. Proc. of the ICML Conf., 1999.
10. Geibel P. & Wysotzki F. (03), Perceptron based learning with example dependent and noisy costs. Proc. of the ICML Conf., 2003.
11. th ICML Conference, 1994.
12. Quinlan J. (86), Induction of decision trees. Machine Learning, 1(1): 81-106, 1986.
13. Quinlan, J. (86b), The effect of noise on concept learning, Machine Learning, Morgan Kaufmann, 1986.
14. Quinlan J. (97), http://rulequest.com/see5-info.html, 1997.
15. Tan M. (93), Cost-sensitive learning of classification knowledge and its applications in robotics. Machine Learning, 13:7-33, 1993.
16. Turney P., Cost-sensitive learning bibliography, http://purl.org/peter. turney/bibliographies/cost-sensitive.html.
17. rd ICDM Conference, 2003.
18. Zhu, X.& Wu, X., (04), Cost-guided Class Noise Handling for Effective Cost-sensitive Learning. Proc. of the ICDM Conf., 2004.
19. Zubek V. & Dietterich T. (02), Pruning improves heuristic search for cost-sensitive learning. Proc. of the ICML Conference, 2000.