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Volumn 20, Issue 5, 2009, Pages 890-896

A novel template reduction approach for the K-nearest neighbor method

Author keywords

Condensing; Cross validation; Editing; Nearest neighbor (KNN); Template reduction

Indexed keywords

CONDENSING; CROSS VALIDATION; EDITING; NEAREST NEIGHBOR (KNN); TEMPLATE REDUCTION;

EID: 67349169047     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2009.2018547     Document Type: Article
Times cited : (113)

References (27)
  • 3
    • 0003684449 scopus 로고    scopus 로고
    • The Elements of Statistical Learning: Data Mining Inference, and Prediction
    • Berlin, Germany: Springer-Verlag
    • T. Hastie, R. Tibshirani, and J. H. Friedman, The Elements of Statistical Learning: Data Mining Inference, and Prediction, ser. Statistics. Berlin, Germany: Springer-Verlag, 2001.
    • (2001) ser. Statistics
    • Hastie, T.1    Tibshirani, R.2    Friedman, J.H.3
  • 5
    • 0034562759 scopus 로고    scopus 로고
    • Similarity based methods: A general framework for classification, approximation and association
    • W. Duch, "Similarity based methods: A general framework for classification, approximation and association," Control Cybern., vol. 29, no. 4, pp. 937-968, 2000.
    • (2000) Control Cybern , vol.29 , Issue.4 , pp. 937-968
    • Duch, W.1
  • 6
    • 0015361129 scopus 로고
    • Asymptotic properties of nearest neighbor rules using edited data
    • Jul
    • D. L. Wilson, "Asymptotic properties of nearest neighbor rules using edited data," IEEE Trans. Syst. Man Cybern., vol. SMC-2, no. 3, pp. 408-420, Jul. 1972.
    • (1972) IEEE Trans. Syst. Man Cybern , vol.SMC-2 , Issue.3 , pp. 408-420
    • Wilson, D.L.1
  • 8
    • 38249009675 scopus 로고
    • Colour image segmentation and labeling through multiedit-condensing
    • F. J. Ferri and E. Vidal, "Colour image segmentation and labeling through multiedit-condensing," Pattern Recognit. Lett., vol. 13, pp. 561-568, 1992.
    • (1992) Pattern Recognit. Lett , vol.13 , pp. 561-568
    • Ferri, F.J.1    Vidal, E.2
  • 9
    • 84931162639 scopus 로고
    • The condensed nearest neighbor rule
    • May
    • P. E. Hart, "The condensed nearest neighbor rule," IEEE Trans. Inf. Theory, vol. IT-14, no. 3, pp. 515-516, May 1968.
    • (1968) IEEE Trans. Inf. Theory , vol.IT-14 , Issue.3 , pp. 515-516
    • Hart, P.E.1
  • 10
    • 0015346497 scopus 로고
    • The reduced nearest neighbor rule
    • May
    • W. Gates, "The reduced nearest neighbor rule," IEEE Trans. Inf. Theory, vol. IT-18, no. 3, pp. 431-433, May 1972.
    • (1972) IEEE Trans. Inf. Theory , vol.IT-18 , Issue.3 , pp. 431-433
    • Gates, W.1
  • 12
    • 0343081513 scopus 로고    scopus 로고
    • Reduction techniques for instance-based learning algorithms
    • D. R. Wilson and T. R. Martinez, "Reduction techniques for instance-based learning algorithms," Mach. Learn., vol. 38, no. 3, pp. 257-286, 2000.
    • (2000) Mach. Learn , vol.38 , Issue.3 , pp. 257-286
    • Wilson, D.R.1    Martinez, T.R.2
  • 13
    • 0036887535 scopus 로고    scopus 로고
    • An efficient prototype merging strategy for the condensed 1-NN rule through class-conditional hierarchical clustering
    • R. A. Mollineda, F. J. Ferri, and E. Vidal, "An efficient prototype merging strategy for the condensed 1-NN rule through class-conditional hierarchical clustering," Pattern Recognit., vol. 35, pp. 2771-2782, 2002.
    • (2002) Pattern Recognit , vol.35 , pp. 2771-2782
    • Mollineda, R.A.1    Ferri, F.J.2    Vidal, E.3
  • 14
    • 0036779076 scopus 로고    scopus 로고
    • Improved k-nearest neighbor classification
    • Y.Wu,K. Ianakiev, and V. Govindaraju, "Improved k-nearest neighbor classification," Pattern Recognit., vol. 35, pp. 2311-2318, 2002.
    • (2002) Pattern Recognit , vol.35 , pp. 2311-2318
    • Wu, Y.1    Ianakiev, K.2    Govindaraju, V.3
  • 15
    • 0347763609 scopus 로고    scopus 로고
    • Using evolutionary algorithms as instance selection for data reduction in KDD: An experimental study
    • Dec
    • J. R. Cano, F. Herrera, and M. Lozano, "Using evolutionary algorithms as instance selection for data reduction in KDD: An experimental study," IEEE Trans. Evol. Comput., vol. 7, no. 6, pp. 561-575, Dec. 2003.
    • (2003) IEEE Trans. Evol. Comput , vol.7 , Issue.6 , pp. 561-575
    • Cano, J.R.1    Herrera, F.2    Lozano, M.3
  • 16
    • 18144451785 scopus 로고    scopus 로고
    • High training set size reduction by space partitioning and prototype abstraction
    • J. S. Sánchez, "High training set size reduction by space partitioning and prototype abstraction," Pattern Recognit., vol. 37, no. 7, pp. 1561-1564, 2004.
    • (2004) Pattern Recognit , vol.37 , Issue.7 , pp. 1561-1564
    • Sánchez, J.S.1
  • 17
    • 25444531678 scopus 로고    scopus 로고
    • Decision boundary preserving prototype selection for nearest neighbor classification
    • R. Barandela, F. J. Ferri, and J. S. Sánchez, "Decision boundary preserving prototype selection for nearest neighbor classification," Int. J. Pattern Recognit. Artif. Intell., vol. 19, no. 6, pp. 787-806, 2005.
    • (2005) Int. J. Pattern Recognit. Artif. Intell , vol.19 , Issue.6 , pp. 787-806
    • Barandela, R.1    Ferri, F.J.2    Sánchez, J.S.3
  • 18
    • 33644879771 scopus 로고    scopus 로고
    • Enhancing density-based data reduction using entropy
    • D. Huang and T. W. S. Chow, "Enhancing density-based data reduction using entropy," Neural Comput., vol. 18, no. 2, pp. 470-495, 2006.
    • (2006) Neural Comput , vol.18 , Issue.2 , pp. 470-495
    • Huang, D.1    Chow, T.W.S.2
  • 19
    • 27744468378 scopus 로고    scopus 로고
    • Learning prototypes and distances: A prototype reduction technique based on nearest neighbor error minimization
    • R. Paredes and E. Vidal, "Learning prototypes and distances: A prototype reduction technique based on nearest neighbor error minimization," Pattern Recognit., vol. 39, no. 2, pp. 171-179, 2006.
    • (2006) Pattern Recognit , vol.39 , Issue.2 , pp. 171-179
    • Paredes, R.1    Vidal, E.2
  • 20
    • 32544437372 scopus 로고    scopus 로고
    • An LVQ-based adaptive algorithm for learning from very small codebooks
    • J. S. Sánchez and A. I. Marqués, "An LVQ-based adaptive algorithm for learning from very small codebooks," Neurocomputing, vol. 69, no. 7-9, pp. 922-927, 2006.
    • (2006) Neurocomputing , vol.69 , Issue.7-9 , pp. 922-927
    • Sánchez, J.S.1    Marqués, A.I.2
  • 21
    • 0036104537 scopus 로고    scopus 로고
    • Advances in instance selection for instance-based learning algorithms
    • H. Brighton and C. Mellish, "Advances in instance selection for instance-based learning algorithms," Data Mining Knowl. Disc., vol. 6, pp. 153-172, 2002.
    • (2002) Data Mining Knowl. Disc , vol.6 , pp. 153-172
    • Brighton, H.1    Mellish, C.2
  • 23
    • 0019610270 scopus 로고
    • The optimal distance measure for nearest neighbor classification
    • Sep
    • R. D. Short and K. Fukunaga, "The optimal distance measure for nearest neighbor classification," IEEE Trans. Inf. Theory, vol. IT-27, no. 5, pp. 622-627, Sep. 1981.
    • (1981) IEEE Trans. Inf. Theory , vol.IT-27 , Issue.5 , pp. 622-627
    • Short, R.D.1    Fukunaga, K.2
  • 24
    • 0033570831 scopus 로고    scopus 로고
    • Combined 5 × 2 cv F test for comparing supervised classification learning algorithms
    • E. Alpaydin, "Combined 5 × 2 cv F test for comparing supervised classification learning algorithms," Neural Comput., vol. 11, pp. 1885-1892, 1999.
    • (1999) Neural Comput , vol.11 , pp. 1885-1892
    • Alpaydin, E.1
  • 25
    • 0000259511 scopus 로고    scopus 로고
    • Approximate statistical tests for comparing supervised classification learning algorithms
    • T. G. Dietterch, "Approximate statistical tests for comparing supervised classification learning algorithms," Neural Comput., vol. 10, pp. 1895-1923, 1998.
    • (1998) Neural Comput , vol.10 , pp. 1895-1923
    • Dietterch, T.G.1
  • 26
    • 0004042460 scopus 로고
    • University of Karlsruhe, Germany, Online, Available: Available
    • L. Prechelt, "Proben1, A set of neural-network benchmark problems," University of Karlsruhe, Germany, 1994 [Online]. Available: Available: http://page.mi.fu-berlin.de/prechelt/ Biblio/1994-21.pdf
    • (1994) Proben1, A set of neural-network benchmark problems
    • Prechelt, L.1
  • 27
    • 0003408496 scopus 로고    scopus 로고
    • Dept. Inf. Comput. Sci, Univ. California, Irvine, Irvine, CA, Online, Available
    • C. L. Blake and C. J. Merz, "UCI Repository of Machine Learning database," Dept. Inf. Comput. Sci., Univ. California, Irvine, Irvine, CA, 1998 [Online]. Available: http://www.ics.uci.edu/~mlearn
    • (1998) UCI Repository of Machine Learning database
    • Blake, C.L.1    Merz, C.J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.