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Volumn , Issue , 2011, Pages 251-254

Using exploratory data analysis for fraud elicitation through supervised learning

Author keywords

exploratory data analysis; fraud elicitation; outlier detection; supervised learning

Indexed keywords

ANOMALY DETECTION; CLASSIFICATION (OF INFORMATION); CRIME; MACHINE LEARNING; STATISTICS; SUPERVISED LEARNING;

EID: 84860372524     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SYNASC.2011.35     Document Type: Conference Paper
Times cited : (3)

References (10)
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  • 6
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    • Procedures for Detecting Outlying Observations in Samples
    • F. E. Grubbs, Procedures for Detecting Outlying Observations in Samples, Technometrics 11: 121, 1969.
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    • Grubbs, F.E.1
  • 7
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    • A survey of outlier detection methodologies
    • V. Hodge, J. Austin, A survey of outlier detection methodologies, Artificial Intelligence Review 22, 2, 85-126, 2004.
    • (2004) Artificial Intelligence Review , vol.22 , Issue.2 , pp. 85-126
    • Hodge, V.1    Austin, J.2
  • 8
    • 0142063407 scopus 로고    scopus 로고
    • Novelty detection: A review - Part 1: Statistical approaches
    • M. Markou, S. Singh, Novelty detection: a review - part 1: statistical approaches, Signal Processing 83(12): 2481-2497, 2003.
    • (2003) Signal Processing , vol.83 , Issue.12 , pp. 2481-2497
    • Markou, M.1    Singh, S.2
  • 9
    • 0142126712 scopus 로고    scopus 로고
    • Novelty detection: A review - Part 2: Neural network based approaches
    • M. Markou, S. Singh, Novelty detection: a review - part 2: neural network based approaches, Signal Processing 83(12): 2499-2521, 2003.
    • (2003) Signal Processing , vol.83 , Issue.12 , pp. 2499-2521
    • Markou, M.1    Singh, S.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.