Rule mining and classification in the presence of feature level and class label ambiguities

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 5803, Issue , 2005, Pages 98-107

  Hewawasam, K K R G K ^a   Premaratne, K ^a   Shyu, M L ^a   Subasingha, S P ^a  

^a University of Miami College of Engineering   (United States)

Author keywords

Association rules; Classification; Data ambiguities; Data mining; Dempster Shafer belief theory; Imperfect data; Missing data

Indexed keywords

ASSOCIATION RULES; CLASSIFICATION; DATA AMBIGUITIES; DEMPSTER-SHAFER BELIEF THEORY; IMPERFECT DATA; MISSING DATA;

ALGORITHMS; DATABASE SYSTEMS; FEATURE EXTRACTION; PATTERN RECOGNITION; RELIABILITY; SENSORS; SET THEORY;

DATA MINING;

EID: 27544507840     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.603993     Document Type: Conference Paper

References (16)

1. A. Motro and P. Smets, eds., Uncertainty Management in Information Systems: From Needs to Solutions, Kluwer Academic Publishers, Boston, MA, 1997.
2. National Science Foundation, "IDM 2004 Workshop," 2004.
3. R. Agrawal and R. Srikant, "Fast algorithms for mining association rules in large databases," in Proc. International Conference on Very Large Data Bases (VLDB'94), pp. 487-499, (Santiago de Chile, Chile), Sept. 1994.
4. B. Liu, W. Hsu, and Y. M. Ma, "Integrating classification and association rule mining," in Proc. ACM SICKDD International Conference on Knowledge Discovery and Data Mining (KDD '98), pp. 80-86, (New York, NY), Aug. 1998.
5. J. Zhang, S. P. Subasingha, K. Premaratne, M.-L. Shyu, M. Kubat, and K. K. R. G. K. Hewawasam, "A novel belief theoretic association rule mining based classifier for handling class label ambiguities," in Foundations in Data Mining (FDM) Workshop, IEEE International Conference on Data Mining (ICDM'04), (Brighton, UK), Nov. 2004.
6. G. Shafer, A Mathematical Theory of Evidence, Princeton University Press, Princeton, NJ, 1976.
7. S. S. Anand, D. A. Bell, and J. G. Hughes, "EDM: A general framework for data mining based on evidence theory," Data and Knowledge Engineering 18, pp. 189-223, 1996.
8. M. Kubat, A. Hafez, V. V. Raghavan, J. R. Lekkala, and W. K. Chen, "Itemset trees for targeted association querying," IEEE Transactions on Knowledge and Data Engineering 15, pp. 1522-1534, Nov./Dec. 2003.
9. M. Deshpande and G. Karypis, "Using conjunction of attribute values for classification," in Proc. International Conference on Information and Knowledge Management (CIKM'02), pp. 356-364, (McLean, VA), Nov. 2002.
10. H. Xu and P. Smets, "Reasoning in evidential networks with conditional belief functions," International Journal of Approximate Reasoning 14, pp. 155-185, Feb./Apr. 1996.
11. R. Fagin and J. Y. Halpern, "A new approach to updating beliefs," in Proc. Conference on Uncertainty in Artificial Intelligence (UAI'91), P. P. Bonissone, M. Henrion, L. N. Kanal, and J. F. Lemmer, eds., pp. 347-374, Elsevier Science, New York, NY, 1991.
12. P. Walley, Statistical Reasoning with Imprecise Probabilities, Chapman and Hall, London, UK, 1991.
13. E. C. Kulasekere, K. Premaratne, D. A. Dewasurendra, M.-L. Shyu, and P. H. Bauer, "Conditioning and updating evidence," International Journal of Approximate Reasoning 36, pp. 75-108, Apr. 2004.
14. I. Bloch, "Some aspects of Dempster-Shafer evidence theory for classification of multi-modality medical images taking partial volume effect into account," Pattern Recognition Letters 17, pp. 905-919, July 1996.
15. C. L. Blake and C. J. Merz, "UCI repository of machine learning databases," 1998.
16. J. R. Quinlan, C4.5: Programs for Machine Learning, Representation and Reasoning Series, Morgan Kaufmann, San Francisco, CA, 1993.