Condensed nearest neighbor data domain description

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 29, Issue 10, 2007, Pages 1746-1758

  Angiulli, Fabrizio ^a  

^a UNIVERSITY OF CALABRIA   (Italy)

Author keywords

Classification; Data condensation; Data domain description; Nearest neighbor rule; Novelty detection

Indexed keywords

CLASSIFICATION; DATA CONDENSATION; DATA DOMAIN DESCRIPTION; NEAREST NEIGHBOR RULE; NOVELTY DETECTION;

CONDENSATION;

STATISTICAL TESTS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; CLUSTER ANALYSIS; DISCRIMINANT ANALYSIS; INFORMATION RETRIEVAL; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; DISCRIMINANT ANALYSIS; INFORMATION STORAGE AND RETRIEVAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 36048938087     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2007.1086     Document Type: Article

References (28)

1. F. Angiulli, "Fast Condensed Nearest Neighbor Rule," Proc. 22nd Int'l Conf. Machine Learning, pp. 7-11, Aug. 2005.
2. F. Angiulli and C. Pizzuti, "Fast Outlier Detection in High-Dimensional Spaces," Proc. European Conf. Principles and Practice of Knowledge Discovery in Databases, pp. 15-26, 2002.
3. M. Breunig, H.P. Kriegel, R. Ng, and J. Sander, "Lof: Identifying Density-Based Local Outliers," Proc. ACM Int'l Conf. Management of Data, 2000.
4. V. Cerverón and F.J. Ferri, "Another Move toward the Minimum Consistent Subset: A Tabu Search Approach to the Condensed Nearest Neighbor Rule," IEEE Trans. Systems, Man, and Cybernetics - Part B: Cybernetics, vol. 31, no. 3, pp. 304-408, 2001.
5. C.-C. Chang and C.-J. Lin, "LIBSVM: A Library for Support Vector Machines," 2001, http://www.csie.ntu.edu.tw/~cjlin/libsvm.
6. T.M. Cover and P.E. Hart, "Nearest Neighbor Pattern Classification," IEEE Trans. Information Theory, vol. 13, pp. 21-27, 1967.
7. B. Dasarathy, "Minimal Consistent Subset (MCS) Identification for Optimal Nearest Neighbor Decision Systems Design," IEEE Trans. Systems, Man, and Cybernetics, vol. 24, no. 3, pp. 511-517, 1994.
8. L. Devroye, "On the Inequality of Cover and Hart," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 3, pp. 75-78, 1981.
9. L. Devroye, L. Gyorfi, and G. Lugosi, A Probabilistic Theory of Pattern Recognition. Springer-Verlag, 1996.
10. C.L. Blake, D.J. Newman, S. Hettich, and C.J. Merz, "UCI Repository of Machine Learning Databases," 1998.
11. E. Eskin, A. Arnold, M. Prerau, L. Portnoy, and S. Stolfo, "A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data," Applications of Data Mining in Computer Security 2002.
12. E. Fix and J. Hodges, "Discriminatory Analysis," Non-Parametric Discrimination: Consistency Properties, Technical Report 4, School of Aviation Medicine, US Air Force (USAF), Randolph Field, Texas, 1951.
13. S. Floyd and M. Warmuth, "Sample Compression, Learnability and the Vapnik-Chervonenkis Dimension," Machine Learning, vol. 21, no. 3, pp. 269-304, 1995.
14. M.R. Garey and D.S. Johnson, Computer and Intractability. W.H. Freeman and Co., 1979.
15. P.E. Hart, "The Condensed Nearest Neighbor Rule," IEEE Trans. Information Theory, vol. 14, pp. 515-516, 1968.
16. D.S. Hochbaum and D.B. Shmoys, "A Best Possible Heuristic for the k Center Problem," Math. Operations Research, vol. 10, no. 2, pp. 180-184, 1985.
17. E. Knorr and R. Ng, "Algorithms for Mining Distance-Based Outliers in Large Datasets," Proc. Int'l Conf. Very Large Databases, pp. 392-403, 1998.
18. N. Littlestone and M. Warmuth, "Relating Data Compression and Learnability," technical report, Univ. of California, Santa Cruz, 1986.
19. S. Ramaswamy, R. Rastogi, and K. Shim, "Efficient Algorithms for Mining Outliers from Large Data Sets," Proc. ACM Int'l Conf. Management of Data, pp. 427-438, 2000.
20. G. Ritter and M.T. Gallegos, "Outliers in Statistical Pattern Recognition and an Application to Automatic Chromosome Classification," Pattern Recognition Letters, vol. 18, pp. 525-539, Apr. 1997.
21. B. Schölkopf, C. Burges, and V. Vapnik, "Extracting Support Data for a Given Task," Proc. Int'l Conf. Knowledge Discovery and Data Mining pp. 251-256, 1995.
22. B. Schölkopf, J. Platt, J. Shawe-Taylor, A.J. Smola, and R.C. Williamson, "Estimating the Support of a High-Dimensional Distribution," Technical Report 87, Microsoft Research, Redmond, Wash., 1999.
23. C. Stone, "Consistent Nonparametric Regression," Annals of Statistics, vol. 8, pp. 1348-1360, 1977.
24. D. Tax and R. Duin, "Data Domain Description Using Support Vectors," Proc. European Symp. Artificial Neural Networks, pp. 251-256, Apr. 1999.
25. D. Tax and R. Duin, "Data Descriptions in Subspaces," Proc. Int'l Conf. Pattern Recognition, pp. 672-675, 2000.
26. D.M.J. Tax, "One-Class Classification," PhD dissertation, Delft Univ. of Technology, June 2001.
27. G. Toussaint, "Proximity Graphs for Nearest Neighbor Decision Rules: Recent Progress," Technical Report SOCS-02.5, School of Computer Science, McGill Univ., Montreal, Québec, 2002.
28. A. Ypma and R. Duin, "Support Objects for Domain Approximation," Proc. Internet Corporation for Assigned Names and Numbers-Conf., 1998.