Survey of improving K-nearest-neighbor for classification

Proceedings - Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007

Volumn 1, Issue , 2007, Pages 679-683

  Jiang, Liangxiao ^a   Cai, Zhihua ^a   Wang, Dianhong ^a   Jiang, Siwei ^a  

^a CHINA UNIVERSITY OF GEOSCIENCES   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION MODEL;

ALGORITHMS; MATHEMATICAL MODELS; PROBLEM SOLVING;

CLASSIFICATION (OF INFORMATION);

EID: 44049090824     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FSKD.2007.552     Document Type: Conference Paper

References (21)

1. D. Aha. Lazy learning. Kluwer Academic Publishers, 1997.
2. D. W. Aha, D. Kibler, and M. K. Albert. Instance-based learning algorithms. Machine Learning, 6:37-66, 1991.
3. J. L. Bentley. Multidimensional binary search trees used for associative searching. Communications of the ACM, 18:509-517, 1975.
4. H. M. P. B. Frank, E. Locally weighted naive bayes. In Proceedings of the Conference on Uncertainty in Artificial Intelligence, pages 249-256. Morgan Kaufmann, 2003.
5. N. Friedman, D. Geiger, and M. Goldszmidt. Bayesian network classifiers. Machine Learning, 29:131-163, 1997.
6. M. J. Greenacre. Theory and Applications of Correspondence Analysis. Academic Press, 1984.
7. K. K. Han. text categorization using weight adjusted k-nearest neighbour classification. Technical report, Dept. of CS, University of Minnesota, 1999.
8. Z. Huang. A fast clustering algorithm to cluster very large categorical data sets in data mining. In In Proc. SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, 1997.
9. L. Jiang, H. Zhang, and Z. Cai. Dynamic k-nearest-neighbor naive bayes with attribute weighted. In Proceedings of the 3rd International Conference on Fuzzy Systems and Knowledge Discovery, pages 365-368. Springer, 2006.
10. L. Jiang, H. Zhang, Z. Cai, and J. Su. Evolutional naive bayes. In Proceedings of the 1st International Symposium on Intelligent Computation and its Applications, pages 344-350. China University of Geosciences Press, 2005.
11. Z. H. S. J. Jiang, L. Instance cloning local naive bayes. In Proceedings of the Eighteenth Canadian Conference on Artificial Intelligence, pages 280-291. Springer, 2005.
12. R. Kohavi. Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pages 202-207. AAAI Press, 1996.
13. R. Kohavi and G. John. Wrappers for feature subset selection. Artificial Intelligence, 97:273-324, 1997.
14. P. Langley, W. Iba, and K. Thomas. An analysis of bayesian classifiers. In Proceedings of the Tenth National Conference of Artificial Intelligence, pages 223-228. AAAI Press, 1992.
15. P. Langley and S. Sage. Induction of selective bayesian classifiers. In Proceedings of Uncertainty in Artificial Intelligence, pages 400-406. Morgan Kaufmann, 1994.
16. T. M. Mitchell. Machine Learning. McGraw-Hill, 1997.
17. C. Stanfill and D. Waltz. Toward memory-based reasoning. Communications of the ACM, 29:1213-1228, 1986.
18. Z. Z. G. I. Webb. Lazy learning of bayesian rules. Machine Learning, 41:53-84, 2000.
19. D. R. Wilson and T. R. Martinez. Improved heterogeneous distance functions. Artificial Intelligence Research, 6:1-34, 1997.
20. I. H. Witten and E. Frank. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco, 2005.
21. Z. Xie, W. Hsu, Z. Liu, and M. Lee. SNNB: A selective neighborhood based naive bayes for lazy learning. In Proceedings of the Sixth Pacific-Asia Conference on KDD, pages 104-114. Springer, 2002.