메뉴 건너뛰기




Volumn , Issue , 2010, Pages 1893-1900

An investigation of real-valued accuracy-based learning classifier systems for electronic fraud detection

Author keywords

Fraud detection; Genetics based machine learning; Learning classifier systems; Network intrusion detection; XCSR

Indexed keywords

FRAUD DETECTION; GENETICS BASED MACHINE LEARNING; LEARNING CLASSIFIER SYSTEM; NETWORK INTRUSION DETECTION; XCSR;

EID: 77955944416     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1830761.1830822     Document Type: Conference Paper
Times cited : (3)

References (26)
  • 7
    • 77955970556 scopus 로고    scopus 로고
    • Ai approaches to fraud detection and risk management
    • T. Fawcett, I. Haimowitz, F. Provost, and S. Stolfo. Ai approaches to fraud detection and risk management. AI Magazine, 19(2):107-108, 1998.
    • (1998) AI Magazine , vol.19 , Issue.2 , pp. 107-108
    • Fawcett, T.1    Haimowitz, I.2    Provost, F.3    Stolfo, S.4
  • 8
    • 58349092406 scopus 로고    scopus 로고
    • Towards adapting xcs for imbalance problems
    • Springer
    • T. H. Nguyen, S. Foitong, P. Srinil, and O. Pinngern. Towards adapting xcs for imbalance problems. In PRICAI '08, pages 1028-1033. Springer, 2008.
    • (2008) PRICAI '08 , pp. 1028-1033
    • Nguyen, T.H.1    Foitong, S.2    Srinil, P.3    Pinngern, O.4
  • 10
    • 55549116330 scopus 로고    scopus 로고
    • Evolutionary rule-based systems for imbalanced data sets
    • A. Orriols-Puig and E. Bernadó-Mansilla. Evolutionary rule-based systems for imbalanced data sets. Soft Computing, 13(3):213-225, 2008.
    • (2008) Soft Computing , vol.13 , Issue.3 , pp. 213-225
    • Orriols-Puig, A.1    Bernadó-Mansilla, E.2
  • 14
    • 36549012987 scopus 로고    scopus 로고
    • An evaluation of naïve bayes variants in content-based learning for spam filtering
    • A. K. Seewald. An evaluation of naïve Bayes variants in content-based learning for spam filtering. Intelligent Data Analysis, 11(5):497-524, 2007.
    • (2007) Intelligent Data Analysis , vol.11 , Issue.5 , pp. 497-524
    • Seewald, A.K.1
  • 15
    • 36749010766 scopus 로고    scopus 로고
    • Biologically-inspired complex adaptive systems approaches to network intrusion detection
    • K. Shafi and H. A. Abbass. Biologically-inspired complex adaptive systems approaches to network intrusion detection. Information Security Technical Report, 12(4):209-217, 2007.
    • (2007) Information Security Technical Report , vol.12 , Issue.4 , pp. 209-217
    • Shafi, K.1    Abbass, H.A.2
  • 16
    • 60949108490 scopus 로고    scopus 로고
    • Intrusion detection with evolutionary learning classifier systems
    • K. Shafi, T. Kovacs, H. A. Abbass, and W. Zhu. Intrusion detection with evolutionary learning classifier systems. Natural Computing, 8(1):3-27, 2009.
    • (2009) Natural Computing , vol.8 , Issue.1 , pp. 3-27
    • Shafi, K.1    Kovacs, T.2    Abbass, H.A.3    Zhu, W.4
  • 18
    • 50249160574 scopus 로고    scopus 로고
    • KDD cup 1999 dataset
    • S. Stolfo et al. KDD cup 1999 dataset. UCI KDD repository. http://kdd.ics.uci.edu, 1999.
    • (1999) UCI KDD Repository
    • Stolfo, S.1
  • 19
    • 0043284110 scopus 로고    scopus 로고
    • For real! XCS with continuous-valued inputs
    • C. Stone and L. Bull. For real! XCS with continuous-valued inputs. Evolutionary Computation, 11(3):299-336, 2003.
    • (2003) Evolutionary Computation , vol.11 , Issue.3 , pp. 299-336
    • Stone, C.1    Bull, L.2
  • 20
    • 58349100775 scopus 로고    scopus 로고
    • Using self-organizing maps with learning classifier system for intrusion detection
    • Springer
    • K. Tamee, P. Rojanavasu, S. Udomthanapong, and O. Pinngern. Using self-organizing maps with learning classifier system for intrusion detection. In PRICAI '08, pages 1071-1076. Springer, 2008.
    • (2008) PRICAI '08 , pp. 1071-1076
    • Tamee, K.1    Rojanavasu, P.2    Udomthanapong, S.3    Pinngern, O.4
  • 21
    • 34447639073 scopus 로고    scopus 로고
    • A new approach to intrusion detection based on an evolutionary soft computing model using neuro-fuzzy classifiers
    • A. N. Toosi and M. Kahani. A new approach to intrusion detection based on an evolutionary soft computing model using neuro-fuzzy classifiers. Computer Communications, 30(10):2201-2212, 2007.
    • (2007) Computer Communications , vol.30 , Issue.10 , pp. 2201-2212
    • Toosi, A.N.1    Kahani, M.2
  • 23
  • 24
    • 20844458491 scopus 로고    scopus 로고
    • Mining with rarity: A unifying framework
    • G. M. Weiss. Mining with rarity: a unifying framework. ACM SIGKDD Explorations Newsletter, 6(1):7-19, 2004.
    • (2004) ACM SIGKDD Explorations Newsletter , vol.6 , Issue.1 , pp. 7-19
    • Weiss, G.M.1
  • 25
    • 0001387704 scopus 로고
    • Classifier fitness based on accuracy
    • S. W. Wilson. Classifier fitness based on accuracy. Evolutionary Computation, 3(2):149-175, 1995.
    • (1995) Evolutionary Computation , vol.3 , Issue.2 , pp. 149-175
    • Wilson, S.W.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.