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Volumn , Issue , 2011, Pages 2690-2697

Improving classification accuracy by identifying and removing instances that should be misclassified

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ACCURACY; DATA SETS; FILTERING METHOD; NOISE REDUCTION METHODS; NOISE REDUCTION TECHNIQUE; OUTLIER DETECTION;

EID: 80054718586     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2011.6033571     Document Type: Conference Paper
Times cited : (101)

References (26)
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    • Hodge, V.1    Austin, J.2
  • 9
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    • Robust decision trees: Removing outliers from databases
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    • John, G.H.1
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    • 78149286827 scopus 로고    scopus 로고
    • Probabilistic noise identification and data cleaning
    • IEEE Comput. Society, November
    • J. M. Kubica and A. Moore. Probabilistic noise identification and data cleaning. In The Third IEEE int. conf. on Data min., pages 131-138. IEEE Comput. Society, November 2003.
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    • Kubica, J.M.1    Moore, A.2
  • 15
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    • Outlier detection algorithms in data mining systems
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    • Petrovskiy, M.I.1
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    • A comparative study of outlier mining and class outlier mining
    • M. K. Saad and N. M. Hewahi. A comparative study of outlier mining and class outlier mining. CS Letters, 1(1), 2009.
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    • An empirical study of instance hardness
    • Submitted to
    • M. R. Smith, T. Martinez, and C. Giraud-Carrier. An empirical study of instance hardness. Submitted to ICML 2011.
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    • Smith, M.R.1    Martinez, T.2    Giraud-Carrier, C.3
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    • An algorithm for correcting mislabeled data
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