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Volumn 5, Issue 2, 2012, Pages 139-150

On XCSR 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: 84861637240     PISSN: 18645909     EISSN: 18645917     Source Type: Journal    
DOI: 10.1007/s12065-012-0076-5     Document Type: Article
Times cited : (11)

References (34)
  • 1
    • 72849145202 scopus 로고    scopus 로고
    • Tech. Rep. 4528.0, Australian Bureau of Statistics, Australian Bureau of Statistics
    • Australian Bureau of Statistics (2008) Personal fraud, 2007. Tech. Rep. 4528.0, Australian Bureau of Statistics.
    • (2008) Personal fraud, 2007
  • 5
    • 0012816981 scopus 로고    scopus 로고
    • An algorithmic description of XCS
    • Illinois Genetic Algorithms Laboratory
    • Butz MV, Wilson SW (2000) An algorithmic description of XCS. Tech. Rep. 2000017, Illinois Genetic Algorithms Laboratory.
    • (2000) Tech. Rep , pp. 2000017
    • Butz, M.V.1    Wilson, S.W.2
  • 8
    • 79958000912 scopus 로고    scopus 로고
    • Detecting credit card fraud by genetic algorithm and scatter search
    • Duman E, Ozcelik MH (2011) Detecting credit card fraud by genetic algorithm and scatter search. Expert Syst Appl 38(10): 13, 057-13, 063.
    • (2011) Expert Syst Appl , vol.38 , Issue.10 , pp. 057-063
    • Duman, E.1    Ozcelik, M.H.2
  • 9
    • 77955970556 scopus 로고    scopus 로고
    • Ai approaches to fraud detection and risk management
    • Fawcett T, Haimowitz I, Provost F, Stolfo S (1998) Ai approaches to fraud detection and risk management. AI Magaz 19(2): 107-108.
    • (1998) AI Magaz , vol.19 , Issue.2 , pp. 107-108
    • Fawcett, T.1    Haimowitz, I.2    Provost, F.3    Stolfo, S.4
  • 12
    • 84898030282 scopus 로고    scopus 로고
    • A study of the effect of different types of noise on the precision of supervised learning techniques
    • Nettleton DF, Orriols-Puig A, Fornells A (2010) A study of the effect of different types of noise on the precision of supervised learning techniques. Artif Intell Rev 33(4): 275-306.
    • (2010) Artif Intell Rev , vol.33 , Issue.4 , pp. 275-306
    • Nettleton, D.F.1    Orriols-Puig, A.2    Fornells, A.3
  • 13
    • 58349092406 scopus 로고    scopus 로고
    • Towards adapting xcs for imbalance problems
    • Springer, Berlin
    • Nguyen TH, Foitong S, Srinil P, Pinngern O (2008) Towards adapting xcs for imbalance problems. In: PRICAI '08, Springer, Berlin, pp 1028-1033.
    • (2008) PRICAI '08 , pp. 1028-1033
    • Nguyen, T.H.1    Foitong, S.2    Srinil, P.3    Pinngern, O.4
  • 14
  • 15
    • 55549116330 scopus 로고    scopus 로고
    • Evolutionary rule-based systems for imbalanced data sets
    • Orriols-Puig A, Bernadó-Mansilla E (2008) Evolutionary rule-based systems for imbalanced data sets. Soft Comput 13(3): 213-225.
    • (2008) Soft Comput , vol.13 , Issue.3 , pp. 213-225
    • Orriols-Puig, A.1    Bernadó-Mansilla, E.2
  • 17
    • 37149053967 scopus 로고    scopus 로고
    • A comprehensive survey of data mining-based fraud detection research
    • Monash University
    • Phua C, Lee V, Smith-Miles K, Gayler R (2005) A comprehensive survey of data mining-based fraud detection research. Tech. rep., Monash University.
    • (2005) Tech. Rep
    • Phua, C.1    Lee, V.2    Smith-Miles, K.3    Gayler, R.4
  • 19
    • 36549012987 scopus 로고    scopus 로고
    • An evaluation of naïve Bayes variants in content-based learning for spam filtering
    • Seewald AK (2007) An evaluation of naïve Bayes variants in content-based learning for spam filtering. Int Data Anal11(5): 497-524.
    • (2007) Int Data Anal , vol.11 , Issue.5 , pp. 497-524
    • Seewald, A.K.1
  • 20
    • 36749010766 scopus 로고    scopus 로고
    • Biologically-inspired complex adaptive systems approaches to network intrusion detection
    • Shafi K, Abbass HA (2007) Biologically-inspired complex adaptive systems approaches to network intrusion detection. Inform Secur Tech Rep 12(4): 209-217.
    • (2007) Inform Secur Tech Rep , vol.12 , Issue.4 , pp. 209-217
    • Shafi, K.1    Abbass, H.A.2
  • 21
    • 60949108490 scopus 로고    scopus 로고
    • Intrusion detection with evolutionary learning classifier systems
    • Shafi K, Kovacs T, Abbass HA, Zhu W (2009) Intrusion detection with evolutionary learning classifier systems. Nat Comput 8(1): 3-27.
    • (2009) Nat Comput , vol.8 , Issue.1 , pp. 3-27
    • Shafi, K.1    Kovacs, T.2    Abbass, H.A.3    Zhu, W.4
  • 23
    • 50249160574 scopus 로고    scopus 로고
    • KDD cup 1999 dataset
    • Stolfo S, et al (1999) KDD cup 1999 dataset. UCI KDD repository. http://kdd. ics. uci. edu.
    • (1999) UCI KDD repository
    • Stolfo, S.1
  • 24
    • 0043284110 scopus 로고    scopus 로고
    • For real! XCS with continuous-valued inputs
    • Stone C, Bull L (2003) For real! XCS with continuous-valued inputs. Evol Comput 11(3): 299-336.
    • (2003) Evol Comput , vol.11 , Issue.3 , pp. 299-336
    • Stone, C.1    Bull, L.2
  • 25
    • 58349100775 scopus 로고    scopus 로고
    • Using self-organizing maps with learning classifier system for intrusion detection
    • Springer, Berlin
    • Tamee K, Rojanavasu P, Udomthanapong S, Pinngern O (2008) Using self-organizing maps with learning classifier system for intrusion detection. In: PRICAI '08, Springer, Berlin, pp 1071-1076.
    • (2008) PRICAI '08 , pp. 1071-1076
    • Tamee, K.1    Rojanavasu, P.2    Udomthanapong, S.3    Pinngern, O.4
  • 26
    • 34447639073 scopus 로고    scopus 로고
    • A new approach to intrusion detection based on an evolutionary soft computing model using neuro-fuzzy classifiers
    • Toosi AN, Kahani M (2007) A new approach to intrusion detection based on an evolutionary soft computing model using neuro-fuzzy classifiers. Comput Commun 30(10): 2201-2212.
    • (2007) Comput Commun , vol.30 , Issue.10 , pp. 2201-2212
    • Toosi, A.N.1    Kahani, M.2
  • 27
    • 69249230890 scopus 로고    scopus 로고
    • Intrusion detection by machine learning: a review
    • Tsai CF, Hsu YF, Lin CY, Lin WY (2009) Intrusion detection by machine learning: a review. Expert Syst Appl 36(10): 11-12.
    • (2009) Expert Syst Appl , vol.36 , Issue.10 , pp. 11-12
    • Tsai, C.F.1    Hsu, Y.F.2    Lin, C.Y.3    Lin, W.Y.4
  • 28
    • 77955380270 scopus 로고    scopus 로고
    • Learning classifier systems: a complete introduction, review, and roadmap
    • Urbanowicz RJ, Moore JH (2009) Learning classifier systems: a complete introduction, review, and roadmap. J Artif Evol App 2009: 1-1125.
    • (2009) J Artif Evol App , vol.2009 , pp. 1-1125
    • Urbanowicz, R.J.1    Moore, J.H.2
  • 30
    • 33646848433 scopus 로고    scopus 로고
    • A game-theoretic approach to credit card fraud detection
    • Vatsa V, Sural S, Majumdar A (2005) A game-theoretic approach to credit card fraud detection. Lect Notes Comput Sci 3803: 263-276.
    • (2005) Lect Notes Comput Sci , vol.3803 , pp. 263-276
    • Vatsa, V.1    Sural, S.2    Majumdar, A.3
  • 31
    • 20844458491 scopus 로고    scopus 로고
    • Mining with rarity: a unifying framework
    • Weiss GM (2004) Mining with rarity: a unifying framework. ACM SIGKDD Explor Newslett 6(1): 7-19.
    • (2004) ACM SIGKDD Explor Newslett , vol.6 , Issue.1 , pp. 7-19
    • Weiss, G.M.1
  • 32
    • 0001387704 scopus 로고
    • Classifier fitness based on accuracy
    • Wilson SW (1995) Classifier fitness based on accuracy. Evol Comput 3(2): 149-175.
    • (1995) Evol Comput , vol.3 , Issue.2 , pp. 149-175
    • Wilson, S.W.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.