Class noise mitigation through instance weighting

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4701 LNAI, Issue , 2007, Pages 708-715

  Rebbapragada, Umaa ^a   Brodley, Carla E ^a  

^a Tufts University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTER ANALYSIS; SET THEORY; VECTORS;

CLASS NOISE MITIGATION; PROBABILITY VECTORS;

PROBABILITY DISTRIBUTIONS;

EID: 38049136824     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74958-5_71     Document Type: Conference Paper

References (16)

1. Brodley, C.E., Friedl, M.A.: Identifying mislabeled training data. JAIR 11, 131-167 (1999)
2. Brodley, C.E., Friedl, M.A.: Identifying and eliminating mislabeled training instances. In: Proc. of the 13th National Conference on Artificial Intelligence, pp. 799-805 (1996)
3. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society 39,1-38 (1977)
4. Gamberger, D., Lavrač, N., Grošelj, C.: Experiments with noise filtering in a medical domain. In: Proc. of the 16th ICML, pp. 143-151 (1999)
5. Gamberger, D., Lavrač;, N., Džeroski, S.: Noise elimination in inductive concept learning: a case study in medical diagnosis. In: 7th Int. Wkshp. on Algorithmic Learning Theory, pp. 199-212 (1996)
6. Lallich, S., Muhlenbach, F., Zighed, D.A.: Improving classification by removing or relabeling mislabeled instances. In: Proc. of the 13th Int. Symp. on the Foundations of Intelligent Systems, pp. 5-15 (2002)
7. Lawrence, N.D., Schölkopf, B,: Estimating a Kernel Fisher Discriminant in the presence of label noise. In: Proc. of the 18th ICML, pp. 306-313 (2001)
8. Muhlenbach, F., Lallich, S., Zighed, D.A.: Identifying and handling mislabelled instances. Journal of Intelligent Information Systems 22, 89-109 (2004)
9. Pelleg, D., Moore, A.: X-means: extending K-means with efficient estimation of the number of clusters. In: Proc. of the 17th ICML, pp. 727-734 (2000)
10. Teng, C.: Correcting noisy data. In: Proc. of the 16th ICML, pp. 239-248 (1999)
11. Verbaeten, S.: Identifying mislabeled training examples in ILP classification problems. In: Proc. of the Machine Learning Conference of Belgium (2002)
12. Verbaeten, S., Van Assche, A.: Ensemble methods for noise elimination in classification problems. In: Multiple Classifier Systems, 4th International Workshop (2003)
13. Witten, I.H., Frank, E.: Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
14. Zeng, X., Martinez, T.: An algorithm for correcting mislabeled data. Intelligent Data Analysis 5 (2001)
15. Zeng, X., Martinez, T.: A noise filtering method using neural networks. In: Proc. of the Int. Wkshp. of Soft Computing Techniques in Instrumentation, Measurement and Related Applications (2003)
16. Zhu, X., Wu, X., Chen, S.: Eliminating class noise in large datasets, In: Proc. of the 20th ICML, pp. 920-927 (2003)