Unbalanced data classification using extreme outlier elimination and sampling techniques for fraud detection

Proceedings of the 15th International Conference on Advanced Computing and Communications, ADCOM 2007

Volumn , Issue , 2007, Pages 511-516

  Maruthi Padmaja, T ^a   Dhulipalla, Narendra ^a   Bapi, Raju S ^b   Radha Krishna, P ^a  

^a INSTITUTE FOR DEVELOPMENT AND RESEARCH IN BANKING TECHNOLOGY   (India)

^b UNIVERSITY OF HYDERABAD   (India)

Author keywords

Data mining; Hybrid sampling; KRNN; SMOTE and fraud detection; Unbalanced dataset

Indexed keywords

CLASSIFICATION (OF INFORMATION); FEEDFORWARD NEURAL NETWORKS; FINANCE; IMAGING TECHNIQUES; INSURANCE; MEDICAL IMAGING; RADIAL BASIS FUNCTION NETWORKS; SAMPLING;

CHALLENGING TASK; DATA MINING; DATA SETS; DATA-CLEANING METHOD; FRAUD DETECTION; HYBRID SAMPLING; INTERNATIONAL CONFERENCES; KRNN; NEAREST NEIGHBORS; NEW APPROACHES; OUTLIER ELIMINATION; SAMPLING TECHNIQUES; SMOTE AND FRAUD DETECTION; TRUE NEGATIVE RATE; TRUE POSITIVE RATE; UNBALANCED DATA; UNBALANCED DATASET; UNDER-SAMPLING;

LAWS AND LEGISLATION;

EID: 52249114541     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. T. Fawcett, and F. Provost, "Adaptive fraud detection,"Data Mining and Knowledge Discovery, Vol.1, 1(3), 1997, pp.1-28.
2. Clifton Phua , V. Lee, K. Smith, and R. Gayler, "A Comprehensive Survey of Data Mining-based Fraud Detection Research, " Artificial Intelligence review, 2005
3. P.Chan, W. Fan, A. Prodromidis, and S.Stolfo, "Distributed Data Mining in Credit Card Fraud Detection," IEEE Intelligent Systems ,Vol. 14, 1999, pp. 67-74,
4. Clifton Phua, A.Damminda, and V. Lee, "Minority Report in Fraud Detection: Classification of Skewed Data," ACM Sigkdd Explorations: Special Issue on Imbalanced Data Sets, 6(1), 2004, pp. 50-59,
5. S.J. Stolfo, D. W. Fan, W. Lee, and A.L Prodromidis, "Credit card fraud detection using meta-learning: Issues and initial results," AAAI Workshop on AI Approaches to Fraud Detection and Risk Management ,AAAI Press, Menlo Park, CA 1997, pp. 83-90.
6. S. Stolfo, A.L. Prodromidis, Shelley Tselepis, Wenke Lee, and D.W. Fan, "JAM: Java agents for meta-learning over distributed databases," AAAI Workshop on AI Approaches to Fraud Detection and Risk Management,AAAI Press, Menlo Park, CA, 1997, pp. 91-98.
7. S.J. Stolfo, Wei Fan, Wenke Lee, A.L. Prodromidis, and Phil Chan, "Cost-based modeling for fraud and intrusion detection: Results from the JAM Project," In Proceedings of the DARPA Information Survivability Conference and Exposition 2,IEEE Computer Press. New York, 1999, pp. 130-144.
8. R.Wheeler, and S.Aitken, "Multiple algorithms for fraud detection. Knowledge-Based Systems," 13(2/3), 2000,pp. 93-99.
9. M.Kubat, and S. Matwin, "Addressing the Curse of Imbalanced Training Sets: One Sided Selection," In Proceedings of the Fourteenth International Conference on Machine Learning, Nashville, Tennesse. Morgan Kaufmann, 1997, pp. 179-186.
10. N.V. Chawla, K.W. Bowyer, L.O. Hall, and W.P. Kegelmeyer, "SMOTE: Synthetic Minority Over-sampling Technique," Journal of Artificial Intelligence Research 16, 2004, pp. 324-357.
11. A. Estabrooks, T. Jo, and N. Japkowicz, "A Multiple Resampling Method for Learning from Imbalances Data Sets,"In Computational Intelligence, Vol. 20, No. 1, 2004
12. Soujanya Vadapalli, Satyanarayana R. Valluri, Kamalakar Karlapalem, "A Simple Yet Effective Data Clustering Algorithm," Sixth International Conference on Data Mining (ICDM'06), 2006, pp. 1108-1112.
13. Randall Wilson, and Tony, " Improved Heterogeneous Distance Functions," Journal of Artificial Intelligence Research 6,1997, pp. 1-34.
14. C. Ling, and C. Li, "Data Mining for Direct Marketing Problems and Solutions," In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98), AAAIPress, New York, 1998, pp. 73-79.
15. R Brause, T.Langsdorf and M. Hepp, "Neural Data Mining for Credit Card Fraud Detection," In Proceedings of 11th IEEE International Conference on Tools with Artificial Intelligence, Illinois, USA, 1999, pp. 103-106.
16. I. Witten, E.Frank, Data Mining: Practical Machine Learning tools and Techniques with Java Implementations, Morgan Kaufmann Publishers, 2000.
17. G.Batista, M. Prati, and M.Monard, "A Study of the behavior of several methods for balancing machine learning training data," ACM SIGKDD Explorations: Special Issue on Imbalanced Data Sets 6(1), 2004, pp. 20-29.