The partitioning- And rule-based filter for noise detection

Proceedings of the 2005 IEEE International Conference on Information Reuse and Integration, IRI - 2005

Volumn 2005, Issue , 2005, Pages 205-210

  Xiao, Yudong ^a   Khoshgoftaar, Taghi M ^a   Seliya, Naeem ^b  

^a Florida Atlantic University   (United States)

^b UNIVERSITY OF MICHIGAN DEARBORN   (United States)

Author keywords

Attribute noise; Class noise; Data filters; Data mining; Noise detection

Indexed keywords

BENCHMARKING; DATA MINING; DATABASE SYSTEMS; SET THEORY; SPURIOUS SIGNAL NOISE;

ATTRIBUTE NOISE; CLASS NOISE; DATA FILTERS; NOISE DETECTION;

KNOWLEDGE BASED SYSTEMS;

EID: 33745683294     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IRI-05.2005.1506474     Document Type: Conference Paper

References (14)

1. C. E. Brodley and M. A. Friedl. Identifying mislabeled training data. Journal of Artificial Intelligence Research, 11:131-167, 1999.
2. W. W. Cohen. Fast effective rule induction. In A. Prieditis and S. Russell, editors, Proceedings of the 12th International Conference on Machine Learning, pages 115-123, Tahoe City, CA, July 1995. Morgan Kaufmann.
3. T. Dasu and T. Johnson. Exploratory Data Mining and Data Cleaning. Wiley series, May 2003.
4. D. Gamberger, N. Lavrač, and S. Džeroski. Noise elimination in inductive concept learning: a case study in medical diagnosis. In Algorithmic Learning Theory, 7th International Workshop, ALT '96, Sydney, Australia, October 1996, Proceedings, volume 1160, pages 199-212. Springer, 1996.
5. D. Gamberger, N. Lavrač, and C. Grošelj. Experiments with noise filtering in a medical domain. In Proceedings of 16th International Conference on Machine Learning, pages 143-151. Morgan Kaufmann, San Francisco, CA, 1999.
6. J. Hipp, U. Güntzer, and U. Grimmer. Data quality mining: Making a virtue of necessity. In Proceedings of 6th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pages 52-57, Santa Barbara, CA, May 2001.
7. T. M. Khoshgoftaar and N. Seliya. The necessity of assuring quality in software measurement data. In Proceedings of 10th International Software Metrics Symposium, pages 119-130, Chicago, IL, September 2004. IEEE Computer Society.
8. T. M. Khoshgoftaar, S. Zhong, and V. Joshi. Noise elimination with ensemble-classifier filtering for software quality estimation. Intelligent Data Analysis: An International Journal, 9(1):3-27, 2005.
9. D. Läbbers, U. Grimmer, and M. Jarke. Systematic development of data mining-based data quality tools. In Proceedings of the 29th VLDB Conference, pages 548-559, Berlin, Germany, 2003.
10. K. Orr. Data quality and systems theory. CACM, 41(2):66-71, February 1998.
11. T. Redman. The impact of poor data quality on the typical enterprise. CACM, 41(2):79-82, February 1998.
12. C. M. Teng. A comparison of noise handling techniques. In Proceedings of the International Florida Artificial Intelligence Research Symposium, pages 269-273, 2001.
13. I. H. Whitten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques with JAVA Implementations. Morgan Kaufmann, San Francisco, CA, 2000.
14. X. Zhu, X. Wu, and Q. Chen. Eliminating class noise in large datasets. In Proceedings of 20th International Conference on Machine Learning, Washington, DC, August 2003.