Noisy data elimination using mutual k-nearest neighbor for classification mining

Journal of Systems and Software

Volumn 85, Issue 5, 2012, Pages 1067-1074

  Liu, Huawen ^a,b   Zhang, Shichao ^c,d  

^a ZHEJIANG NORMAL UNIVERSITY   (China)

^b Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education   (China)

^c GUANGXI NORMAL UNIVERSITY   (China)

^d UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

Data mining; Data reduction; kNN; Mutual nearest neighbor; Pattern classification

Indexed keywords

DATA MINING; DATA REDUCTION; MEMBERSHIP FUNCTIONS; MOTION COMPENSATION; NEAREST NEIGHBOR SEARCH; PATTERN RECOGNITION; TEXT PROCESSING;

CLASSIFICATION MINING; EXPERIMENTAL ANALYSIS; K NEAREST NEIGHBOR (KNN); K-NEAREST NEIGHBORS; LARGE-SCALE DATABASE; MUTUAL K-NEAREST NEIGHBORS; NEAREST NEIGHBORS; PSEUDO NEAREST NEIGHBORS;

LEARNING ALGORITHMS;

EID: 84865233213     PISSN: 01641212     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jss.2011.12.019     Document Type: Article

References (37)

1. Asuncion, A., Newman, DJ., 2007. UCI Repository of Ma-chine Learning Databases, Department of Information and Computer Science. University of California, Irvine, Available at: http://www.ics.uci.edu/∼mlearn/ MLRepository.html.
2. Bhatia, N., Vandana, 2010. Survey of nearest neighbor techniques. International Journal of Computer Science and Information Security 8 (2), 302-305 arxiv:1007.0085v1.
3. Brighton, H., Mellish, C, 2002. Advances in instance selection for instance-based learning algorithms. Data Mining and Knowledge Discovery 6, 153-172. (Pubitemid 37113870)
4. Chandola, V., Banerjee, A., Kumar, V., 2009. Anomaly detection: a survey. ACM Computing Surveys 41 (3), 1-58.
5. Chapman, A.D., (2005). Principles and Methods of Data Cleaning-Primary Species and Species-Occurrence Data, version 1.0. Report for the Global Biodiversity Information Facility, Copenhagen.
6. Chen, Y.S., Hung, Y.P., Yen, T.F., Fuh, CS., 2007. Fast and versatile algorithm for nearest neighbor search based on a lower bound tree. Pattern Recognition 40, 360-375.
7. Cohen, W.W., 1995. Fast effective rule induction. In: Proceedings ofthe 12th International Conference on Machine Learning, pp. 115-123.
8. Cover, T.M., Hart, P.E., 1967. Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13, 21-27.
9. Davidson, I., Tayi, G., 2009. Data preparation using data quality matrices for classification mining. European Journal of Operational Research 197, 764-772.
10. Ding, C.H.Q., He, X., 2004. K-nearest-neighbor consistency in data clustering: incorporating local information into global optimization. In: Proceedings of ACM Symposium on Applied Computing (SAC), pp. 584-589.
11. Domeniconi, C, Peng, J., Gunopulos, D., 2002. Locally adaptive metric nearest-neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (9), 1281-1285. (Pubitemid 35242588)
12. Duda, R.O., Hart, P.E., Stork, D.G., 2001. Pattern Classification, 2nd ed. Wiley, New York.
13. Fayed, H.A., Atiya, A.F., 2009. A novel template reduction approach forthe k-nearest neighbor method. IEEE Transactions on Neural Networks 20 (5), 890-896.
14. Gao, Y., Zheng, B., Chen, G., Li, Q., Chen, C, Chen, G., 2010. Efficient mutual nearest neighbor query processing for moving object trajectories. Information Sciences 180, 2170-2195.
15. Garcia-Laencina, P.J., Sancho-Gomez, J.L., Figueiras-Vidal, A.R., 2010. Pattern classification with missing data: a review. Neural Computing & Applications 19, 263-282.
16. Gowda, K.C., Krishna, G., 1979. The condensed nearest neighbor rule using the concept of mutual nearest neighborhood. IEEE Transactions on Information Theory 25 (4), 488-490. (Pubitemid 9477344)
17. Hulse, J.V., Khoshgoftaar, T., 2009. Knowledge discovery from imbalanced and noisy data. Data and Knowledge Engineering 68, 1513-1542.
18. Jahromi, M.Z., Parvinnia, E., John, R., 2009. A method of learning weighted similarity function to improve the performance of nearest neighbor. Information Sciences 179, 2964-2973.
19. Jin, W., Tung, A.K.H., Han, J., Wang, W., 2006. Ranking outliers using symmetric neighborhood relationship. In: Proceedings ofthe 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 577-593. (Pubitemid 44019461)
20. Latourrette, M., 2000. Toward an explanatory similarity measure for nearest-neighbor classification. In: Proc. ofthe 11th European Conference on Machine Learning, London, UK, pp. 238-245.
21. Lee, H.K.H., Taddy, M., Gray, G.A., 2010. Selection of a representative sample. Journal of Classification 27, 41-53.
22. Lindenbaum, M., Markovitch, S., Rusakov, D., 2004. Selective sampling for nearest neighbor classifiers. Machine Learning 54, 125-152.
23. Liu, H., Liu, L, Zhang, H., 2010. Ensemble gene selection for cancer classification. Pattern Recognition 43 (8), 2763-2772.
24. Liu, H., Sun, J., Liu, L., Zhang, H., 2009. Feature selection with dynamic mutual information. Pattern Recognition 42, 1330-1339.
25. Marchiori, E., 2010. Class conditional nearest neighbor for large margin instance selection. IEEE Transactions on Pattern Analysis and Machine Intelligence 32 (2), 364-370.
26. Olvera-Lopez, J.A., Carrasco-Ochoa, J.A., Martinez-Trinidad, J.F., Kittler, J., 2010. A review ofinstance selection methods. Artificial Intelligence Review34, 133-143.
27. Pyle, D., 1999. Data Preparation for Data Mining. Morgan Kaufmann Publishers, San Francisco, CA, USA.
28. Song, Y., Huang, J., Zhou, D., Zha, H., Giles, CL, 2007. IKNN: informative k-nearest neighbor pattern classification. In: PKDD 2007, LNAI 4702, pp. 248-264.
29. Toyama, J., Kudo, M., Imai, H., 2010. Probably correct k-nearest neighbor search in high dimensions. Pattern Recognition 43, 1361-1372.
30. Wilson, D.R., Martinez, T.R., 1997. Improved heterogeneous distance functions. Journal of Artificial Intelligence Research 6, 1-34. (Pubitemid 127667584)
31. Witten, I.H., Frank, E., 2005. Data Mining-Practical Machine Learning Tools and Techniques with JAVA Implementations, 2nd ed. Morgan Kaufmann Publishers, Los Altos.
32. Wu, X., Kumar, V., Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLach-lan, G., Ng, A., Liu, B., Yu, P., Zhou, Z., Steinbach, M., Hand, D., Stein-berg, D., 2008. Top 10 algorithms in data mining. Knowledge and Information Systems 14, 1-37.
33. Yang, Q., Wu, X., 2006. 10 challenging problems in data mining research. International Journal of Information Technology and Decision Making 5 (4), 597-604.
34. Yu, H., 2011. Selective sampling techniques for feedback-based data retrieval. Data Mining and Knowledge Discovery 22(1), 1-30.
35. Zhang, Y., Wu, X., 2010. Integrating induction and deduction for noisy data mining. Information Sciences 180 (14), 2663-2673.
36. Zhang, S., 2011. Shell-neighbor method and its application in missing data imputation. Applied Intelligence 35 (1), 123-133.
37. Zollanvari, A., Braga-Neto, U.M., Dougherty, E.R., 2010. Joint sampling distribution between actual and estimated classification errors for linear discriminant analysis. IEEE Transactions on Information Theory 56 (2), 784-804.