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Volumn 46, Issue 2, 2013, Pages 472-482

On the study of nearest neighbor algorithms for prevalence estimation in binary problems

Author keywords

Methodology; Nearest neighbor; Prevalence estimation; Quantification

Indexed keywords

BINARY PROBLEMS; METHODOLOGY; NEAREST NEIGHBOR ALGORITHM; NEAREST NEIGHBORS; PREVALENCE ESTIMATION; QUANTIFICATION; ROBUST ALGORITHM; STATISTICAL DIFFERENCES;

EID: 84867399462     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2012.07.022     Document Type: Article
Times cited : (40)

References (26)
  • 4
    • 61849140244 scopus 로고    scopus 로고
    • Weighted k-Nearest-Neighbor Techniques and Ordinal Classification
    • Ludwig-Maximilians University, Munich
    • K. Hechenbichler, K. Schliep, Weighted k-Nearest-Neighbor Techniques and Ordinal Classification, Technical Report 399 (SFB 386), Ludwig-Maximilians University, Munich, 2004.
    • (2004) Technical Report 399 (SFB 386)
    • Hechenbichler, K.1    Schliep, K.2
  • 7
    • 33947681316 scopus 로고    scopus 로고
    • ML-KNN a lazy learning approach to multi-label learning
    • M. Zhang, and Z. Zhou ML-KNN a lazy learning approach to multi-label learning Pattern Recognition 40 2007 2038 2048
    • (2007) Pattern Recognition , vol.40 , pp. 2038-2048
    • Zhang, M.1    Zhou, Z.2
  • 8
    • 50549093309 scopus 로고    scopus 로고
    • Quantifying counts and costs via classification
    • G. Forman Quantifying counts and costs via classification Data Mining and Knowledge Discovery 17 2008 164 206
    • (2008) Data Mining and Knowledge Discovery , vol.17 , pp. 164-206
    • Forman, G.1
  • 10
    • 14844366200 scopus 로고    scopus 로고
    • On the application of ROC analysis to predict classification performance under varying class distributions
    • G. Webb, and K. Ting On the application of ROC analysis to predict classification performance under varying class distributions Machine Learning 58 2005 25 32
    • (2005) Machine Learning , vol.58 , pp. 25-32
    • Webb, G.1    Ting, K.2
  • 11
    • 14844357975 scopus 로고    scopus 로고
    • A response to Webb and Tings on the application of ROC analysis to predict classification performance under varying class distributions
    • T. Fawcett, and P. Flach A response to Webb and Tings on the application of ROC analysis to predict classification performance under varying class distributions Machine Learning 58 2005 33 38
    • (2005) Machine Learning , vol.58 , pp. 33-38
    • Fawcett, T.1    Flach, P.2
  • 12
    • 33646391662 scopus 로고    scopus 로고
    • Counting positives accurately despite inaccurate classification
    • Springer
    • G. Forman, Counting positives accurately despite inaccurate classification, in: Proceedings of the 16th ECML, Springer, 2005, pp. 564-575.
    • (2005) Proceedings of the 16th ECML , pp. 564-575
    • Forman, G.1
  • 13
    • 33749582214 scopus 로고    scopus 로고
    • Quantifying trends accurately despite classifier error and class imbalance
    • ACM
    • G. Forman, Quantifying trends accurately despite classifier error and class imbalance, in: Proceedings of the 12th SIGKDD, ACM, 2006, pp. 157-166.
    • (2006) Proceedings of the 12th SIGKDD , pp. 157-166
    • Forman, G.1
  • 14
    • 0037410687 scopus 로고    scopus 로고
    • Choosing k for two-class nearest neighbour classifiers with unbalanced classes
    • D. Hand, and V. Vinciotti Choosing k for two-class nearest neighbour classifiers with unbalanced classes Pattern Recognition Letters 24 2003 1555 1562
    • (2003) Pattern Recognition Letters , vol.24 , pp. 1555-1562
    • Hand, D.1    Vinciotti, V.2
  • 15
    • 0000887162 scopus 로고
    • Choice of the smoothing parameter and efficiency of k-nearest neighbor classification
    • G.G. Enas, and S.C. Choi Choice of the smoothing parameter and efficiency of k-nearest neighbor classification Computers & Mathematics with Applications 12 1986 235 244
    • (1986) Computers & Mathematics with Applications , vol.12 , pp. 235-244
    • Enas, G.G.1    Choi, S.C.2
  • 16
    • 48149111512 scopus 로고    scopus 로고
    • Locally linear reconstruction for instance-based learning
    • P. Kang, and S. Cho Locally linear reconstruction for instance-based learning Pattern Recognition 41 2008 3507 3518
    • (2008) Pattern Recognition , vol.41 , pp. 3507-3518
    • Kang, P.1    Cho, S.2
  • 18
    • 17844387127 scopus 로고    scopus 로고
    • Neighbor-weighted k-nearest neighbor for unbalanced text corpus
    • S. Tan Neighbor-weighted k-nearest neighbor for unbalanced text corpus Expert Systems with Applications 28 2005 667 671
    • (2005) Expert Systems with Applications , vol.28 , pp. 667-671
    • Tan, S.1
  • 19
    • 34250080806 scopus 로고
    • A weighted nearest neighbor algorithm for learning with symbolic features
    • S. Cost, and S. Salzberg A weighted nearest neighbor algorithm for learning with symbolic features Machine Learning 10 1993 57 78
    • (1993) Machine Learning , vol.10 , pp. 57-78
    • Cost, S.1    Salzberg, S.2
  • 20
    • 0033100624 scopus 로고    scopus 로고
    • A new nearest-neighbor rule in the pattern classification problem
    • K. Hattori, and M. Takahashi A new nearest-neighbor rule in the pattern classification problem Pattern Recognition 32 1999 425 432
    • (1999) Pattern Recognition , vol.32 , pp. 425-432
    • Hattori, K.1    Takahashi, M.2
  • 21
    • 28544439958 scopus 로고    scopus 로고
    • An effective refinement strategy for KNN text classifier
    • S. Tan An effective refinement strategy for KNN text classifier Expert Systems with Applications 30 2006 290 298
    • (2006) Expert Systems with Applications , vol.30 , pp. 290-298
    • Tan, S.1
  • 23
    • 0033570831 scopus 로고    scopus 로고
    • Combined 5×2 cv F test for comparing supervised classification learning algorithms
    • E. Alpaydm Combined 5×2 cv F test for comparing supervised classification learning algorithms Neural computation 11 1999 1885 1892
    • (1999) Neural Computation , vol.11 , pp. 1885-1892
    • Alpaydm, E.1
  • 24
    • 0000259511 scopus 로고    scopus 로고
    • Approximate statistical tests for comparing supervised classification learning algorithms
    • T.G. Dietterich Approximate statistical tests for comparing supervised classification learning algorithms Neural Computation 10 1998 1895 1923
    • (1998) Neural Computation , vol.10 , pp. 1895-1923
    • Dietterich, T.G.1
  • 26
    • 29644438050 scopus 로고    scopus 로고
    • Statistical comparisons of classifiers over multiple data sets
    • J. Demšar Statistical comparisons of classifiers over multiple data sets Journal of Machine Learning Research 7 2006 1 30
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 1-30
    • Demšar, J.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.