On the Selection of the Globally Optimal Prototype Subset for Nearest-Neighbor Classification

References

• Altschul S. F., Boguski M. S., Gish W., Wootton J. C. Issues in searching molecular sequence databases. Nature Genetics (1994) 6:119–129Crossref, Google Scholar
• Altschul S. F., Gish W., Miller W., Myers E. W., Lipman D. J. Basic local alignment search tool. J. Molecular Biol. (1990) 215:403–410Crossref, Google Scholar
• Bennett M. V., Willemain T. R. The filtered nearest neighbor method for generating low-discrepancy sequences. INFORMS J. Comput. (2004) 16:68–72Link, Google Scholar
• Bezdek J. C., Kuncheva L. I. Nearest prototype classifier designs: An experimental study. Internat. J. Intelligent Systems (2001) 16:1445–1473Crossref, Google Scholar
• Blake C. L., Merz C. J. UCI Repository of Machine Learning Databases. (1998) . Department of Information and Computer Sciences, University of California, Irvine, Irvine, CA, http://www.ics.uci.edu/∼mlearn/MLRepository.htmlGoogle Scholar
• Breiman L., Friedmann J. H., Olshen R. A., Stone C. J.Classification and Regression Trees (1984) (Wadsworth, Belmont, CA) Google Scholar
• Brighton H., Mellish C. Advances in instance selection for instance-based learning algorithms. Data Mining and Knowledge Discovery (2002) 6:153–172Crossref, Google Scholar
• Carrizosa E., Martin-Barragan B., Plastria F., Romero Morales D. A dissimilarity-based approach for classification. (2005) . Technical report, METEOR Research Memorandum RM/02/027, University of Maastricht, The NetherlandsGoogle Scholar
• Cochran W. G.Sampling Techniques (1977) 3rd ed.(Wiley, New York) Google Scholar
• Cover T. M., Hart P. E. Nearest neighbor pattern classification. IEEE Trans. Inform. Theory (1967) 13:21–27Crossref, Google Scholar
• Cristianini N., Shawe-Taylor J.An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods (2000) (Cambridge University Press, Cambridge, UK) Crossref, Google Scholar
• Dasarathy B. V.Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques (1991) (IEEE Computer Society Press, Los Alamitos, CA) Google Scholar
• Devroye L., Györfi L., Lugosi G.A Probabilistic Theory of Pattern Recognition (1996) (Springer, New York) Crossref, Google Scholar
• Freed N., Glover F. Simple but powerful goal programming models for discriminant problems. Eur. J. Oper. Res. (1981) 7:44–60Crossref, Google Scholar
• Garey M. R., Johnson D. S.Computers and Intractability: A Guide to the Theory of NP-Completeness (1979) (W. H. Freeman, New York) Google Scholar
• Gehrlein W. V. General mathematical programming formulations for the statistical classification problem. Oper. Res. Lett. (1986) 5:299–304Crossref, Google Scholar
• Geva S., Sitte J. Adaptive nearest neighbor pattern classifier. IEEE Trans. Neural Networks (1991) 2:318–322Crossref, Google Scholar
• Gochet W., Stam A., Srinivasan V., Chen S. X. Multigroup discriminant analysis using linear programming. Oper. Res. (1997) 45:213–225Link, Google Scholar
• Hansen P., Mladenović N. Variable neighborhood search for the p-median. Location Sci. (1998) 5:207–226Crossref, Google Scholar
• Hansen P., Mladenović N. Variable neighborhood decomposition search. J. Heuristics (2001a) 7:335–350Crossref, Google Scholar
• Hansen P., Mladenović N. Variable neighborhood search: Principles and applications. Eur. J. Oper. Res. (2001b) 130:449–467Crossref, Google Scholar
• Hart P. E. The condensed nearest neighbor rule. IEEE Trans. Inform. Theory (1968) 14:515–516Crossref, Google Scholar
• Hastie T., Tibshirani R., Friedman J.The Elements of Statistical Learning (2001) (Springer, New York) Crossref, Google Scholar
• Kaufman L., Rousseeuw P. J.Finding Groups in Data. An Introduction to Cluster Analysis (1990) (Wiley, New York) Crossref, Google Scholar
• King R. D., Feng C., Sutherland A. Statlog: Comparison of classification algorithm in large real-world problems. Appl. Artificial Intelligence (1995) 9:289–333Crossref, Google Scholar
• Kohavi R. Cross-validation and bootstrap for accuracy estimation and model selection. Proc. 14th Internat. Joint Conf. Artificial Intelligence (1995) (Morgan Kaufmann, San Fransisco, CA) 1137–1143Google Scholar
• Kuncheva L. I. Fitness function in editing k-NN reference set by genetic algorithms. Pattern Recognition (1997) 30:1041–1049Crossref, Google Scholar
• Kuncheva L. I., Bezdek J. C. Nearest prototype classification: Clustering, genetic algorithm or random search? IEEE Trans. Systems, Man, and Cybernetics, Part C (1998) 28:160–164Crossref, Google Scholar
• Lipowezky U. Selection of the optimal prototype subset for 1-NN classification. Pattern Recognition Lett. (1998) 19:907–918Crossref, Google Scholar
• Mangasarian O. L. Misclassification minimization. J. Global Optim. (1994) 5:309–323Crossref, Google Scholar
• McLachlan G. J.Discriminant Analysis and Statistical Pattern Recognition (1992) (Wiley, New York) Crossref, Google Scholar
• Pearson W. R., Lipman D. J. Improved tools for biological sequence comparison. Proc. Natl. Acad. Sci. (1988) 85:2444–2448Crossref, Google Scholar
• Pekalska E., Duin R. P. W., Paclík P. Prototype selection for dissimilarity-based classifiers. Pattern Recognition (2006) 39:189–208Crossref, Google Scholar
• Plastria F., Drezner Z. Continuous location problems. Facility Location. A Survey of Applications and Methods (1995) (Springer-Verlag, New York) 229–266Crossref, Google Scholar
• Plastria F. Asymmetric distances, semidirected networks and majority in Fermat-Weber problems. Locator: ePublication of Location Analysis (2001) 2:15–62Google Scholar
• Plastria F. Formulating logical implications in combinatorial optimisation. Eur. J. Oper. Res. (2002) 140:338–353Crossref, Google Scholar
• Thompson S. K.Sampling (2002) (Wiley, New York) Google Scholar
• Yang M.-S., Shih H.-M. Cluster analysis based on fuzzy relations. Fuzzy Sets and Systems (2001) 120:197–212Crossref, Google Scholar
• Zimmermann H. J.Fuzzy Set Theory and Its Applications (1991) (Kluwer, Dordrecht, The Netherlands) Crossref, Google Scholar