Efficient model selection for large-scale nearest-neighbor data mining

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6121 LNCS, Issue , 2012, Pages 37-54

  Hamerly, Greg ^a   Speegle, Greg ^a  

^a BAYLOR UNIVERSITY   (United States)

Author keywords

data mining; k nearest neighbor; optimal parameter selection

Indexed keywords

CROSS VALIDATION; DENSITY ESTIMATION; IMPROVING PERFORMANCE; K NEAREST NEIGHBOR; K-NEAREST NEIGHBORS; K-VALUES; LARGE DATASETS; LINEAR REGRESSION PROBLEMS; MODEL SELECTION; NEAREST-NEIGHBORS; OPTIMAL PARAMETER SELECTION; PRIORI KNOWLEDGE;

ALGORITHMS; INFORMATION RETRIEVAL; LAW ENFORCEMENT; OPTIMIZATION; SECURITY OF DATA;

DATA MINING;

EID: 84857480981     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-25704-9_6     Document Type: Conference Paper

References (33)

1. Arya, S., Mount, D., Netanyahu, N., Silverman, R., Wu, A.: An optimal algorithm for approximate nearest neighbor searching in fixed dimensions. Journal of the ACM 45(6), 891-923 (1999)
2. Blockeel, H., Struyf, J.: Efficient algorithms for decision tree cross-validation. In: International Conference on Machine Learning, pp. 11-18 (2001)
3. Böhm, C., Berchtold, S., Keim, D.A.: Searching in high-dimensional spaces: Index structures for improving the performance of multimedia databases. ACM Computing Surveys 33(3), 322-373 (2001)
4. Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1), 21-27 (1967)
5. Devroye, L., Györfi, L., Lugosi, G.: A Probabilistic Theory of Pattern Recognition. Springer, Heidelberg (1996)
6. Ferrer-Troyano, F.J., Aguilar-Ruiz, J.S., Riquelme, J.-C.: Empirical Evaluation of the Difficulty of Finding a Good Value of k for the Nearest Neighbor. In: Sloot, P.M.A., Abramson, D., Bogdanov, A.V., Gorbachev, Y.E., Dongarra, J., Zomaya, A.Y. (eds.) ICCS 2003. LNCS, vol. 2658, pp. 766-773. Springer, Heidelberg (2003)
7. Friedman, J.H., Bentley, J.L., Finkel, R.A.: Two algorithms for nearest-neighbor search in high dimensions. ACM Transactions on Mathematical Software 3(3), 209-226 (1977)
8. Geisser, S.: The predictive sample reuse method with applications. Journal of the American Statistical Association 70(350), 320-328 (1975)
9. Ghosh, A., Chaudhuri, P., Murthy, C.: On visualization and aggregation of nearest neighbor classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(10), 1592-1602 (2005)
10. Ghosh, A.K.: On nearest neighbor classification using adaptive choice of k. Journal of Computational and Graphical Statistics 16(2), 482-502 (2007)
11. Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: International Conference on Very Large Data Bases, pp. 518-529 (1999)
12. Golub, G.H., Van Loan, C.F.: Matrix Computations, 2nd edn. Johns Hopkins University Press (1996)
13. Guttman, A.: R-trees: A dynamic index structure for spatial searching. In: ACM International Conference on Management of Data, pp. 47-57 (1984)
14. Hastie, T., Tibshirani, R.: Discriminant adaptive nearest neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(6), 607-616 (1996)
15. Indyk, P., Motwani, R.: Approximate nearest neighbors: Towards removing the curse of dimensionality. In: ACM Symposium on Theory of Computation, pp. 604-613 (1998)
16. Li, K.-C.: Asymptotic optimality for cp, cl, cross-validation, and generalized cross-validation: Discrete index set. The Annals of Statistics 15(3), 958-975 (1987)
17. Li, L., Weinberg, C., Darden, T., Pederson, L.: Gene selection for sample classification based on gene expression data: Study of sensitivty to choice of parameters of the ga/knn method. Bioinformatics 17(12), 1131-1142 (2001)
18. Lin, K.-I., Jagadish, H., Faloutsos, C.: The TV-tree: An index structure for high-dimensional data. The International Journal on Very Large Databases 3(4), 517-542 (1994)
19. Moore, A., Lee, M.S.: Efficient algorithms for minimizing cross validation error. In: International Conference on Machine Learning, pp. 190-198 (1994)
20. Mount, D.M., Arya, S.: ANN: A library for approximate nearest neighbor searching (2006), http://www.cs.umd.edu/~mount/ANN/
21. Mullin, M., Sukthankar, R.: Complete cross-validation for nearest neighbor classifiers. In: International Conference on Machine Learning, pp. 639-646. Morgan Kaufmann (2000)
22. Olsson, J.S.: An analysis of the coupling between training set and neighborhood sizes of the knn classifier. In: SIGIR, pp. 685-686 (2006)
23. Ouyang, D., Li, D., Li, Q.: Cross-validation and non-parametric k nearest-neighbor estimation. Econometrics Journal 9, 448-471 (2006)
24. Racine, J.: Feasible cross-validatory model selection for general stationary processes. Journal of Applied Econometrics 12(2), 169-179 (1997)
25. Shakhnarovich, G., Indyk, P., Darrell, T. (eds.): Nearest-Neighbor Methods in Learning and Vision. MIT Press (2006)
26. Stone, M.: Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, Series B 36(2), 111-147 (1974)
27. Struyf, J., Blockeel, H.: Efficient Cross-Validation in ILP. In: Rouveirol, C., Sebag, M. (eds.) ILP 2001. LNCS (LNAI), vol. 2157, pp. 228-239. Springer, Heidelberg (2001)
28. Uhlmann, J.K.: Satisfying general proximity/similarity queries with metric trees. Applied Mathematics Letters 4, 175-179 (1991)
29. Wang, J., Neskovic, P., Cooper, L.N.: Neighborhood size selection in the k-nearest-neighbor rule using statistical confidence. The Journal of the Pattern Recognition Society 39(3), 417-423 (2006)
30. Wettschereck, D., Dietterich, T.G.: Locally adaptive nearest neighbor algorithms. Advances in Neural Information Processing Systems 6, 184-191 (1994)
31. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques (1999)
32. Wu, X., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Yu, P.S., Zhou, Z.-H., Steinbach, M., Hand, D.J., Steinberg, D.: Top 10 algorithms in data mining. Knowledge and Information Systems 14(1), 1-37 (2008)
33. Xue, G.-R., Lin, C., Yang, Q., Xi, W., Zeng, H.-J., Yu, Y., Chen, Z.: Scalable collaborative filtering using cluster-based smoothing. In: SIGIR, pp. 114-121 (2005)