Using relational knowledge discovery to prevent securities fraud

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2005, Pages 449-458

  Neville, Jennifer ^a   Şimşek, Özgür ^a   Jensen, David ^a   Komoroske, John ^b   Palmer, Kelly ^b   Goldberg, Henry ^b  

^a Biologically Inspired Neural and Dynamical Systems Laboratory   (United States)

^b National Association of Securities Dealers   (United States)

Author keywords

Fraud detection; Relational probability trees; Statistical relational learning

Indexed keywords

DATA ACQUISITION; LEARNING ALGORITHMS; PROBABILISTIC LOGICS; SECURITY OF DATA; STATISTICAL METHODS;

DATASETS; FRAUD DETECTION; RELATIONAL PROBABILITY TREES; STATISTICAL RELATIONAL LEARNING;

KNOWLEDGE BASED SYSTEMS;

EID: 32344439564     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1081870.1081922     Document Type: Conference Paper

References (16)

1. H. Blau, N. Immerman, and D. Jensen. A visual query language for relational knowledge discovery. Technical Report TR-01-28, University of Massachusetts Amherst, Computer Science Dept., 2001.
2. C. Brodley and P. Smyth. Applying classification algorithms in practice. Statistics and Computing, 7:45-56, 1997.
3. S. Chakrabarti, B. Dom, and P. Indyk. Enhanced hypertext categorization using hyperlinks. In Proc. of the ACM SIGMOD International Conference on Management of Data, pages 307-318, 1998.
4. C. Cortes, D. Pregibon, and C. Volinsky. Communities of interest. In Proc. of the 4th International Symposium of Intelligent Data Analysis, 2001.
5. T. Fawcett and F. Provost. Adaptive fraud detection. Data Mining and Knowledge Discovery, 1:291-316, 1997.
6. U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. From data mining to knowledge discovery in databases. AI Magazine, Fall:37-54, 1996.
7. L. Getoor, N. Friedman, D. Koller, and A. Pfeffer. Learning probabilistic relational models. In Relational Data Mining. Springer-Verlag, 2001.
8. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag, 2000.
9. D. Jensen and J. Neville. Linkage and autocorrelation cause feature selection bias in relational learning. In Proc. of the 19th International Conference on Machine Learning, pages 259-266, 2002.
10. D. Jensen and J. Neville. Avoiding bias when aggregating relational data with degree disparity. In Proc. of the 20th International Conference on Machine Learning, pages 274-281, 2003.
11. D. Jensen, J. Neville, and B. Gallagher. Why collective inference improves relational classification. In Proc. of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 593-598, 2004.
12. J. Neville, D. Jensen, L. Friedland, and M. Hay. Learning relational probability trees. In Proc. of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 625-630, 2003.
13. F. Provost and P. Domingos. Tree induction for probability-based rankings. Machine Learning, 52:3, 2003.
14. F. Provost and T. Fawcett. Analysis and visualization of classifier performance: Comparison under imprecise class and cost distributions. In Proc. of the 3rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 43-48, 1997.
15. S. Sanghai, P. Domingos, and D. Weld. Dynamic probabilistic relational models. In Proc. of the 18th International Joint Conference on Artificial Intelligence, pages 992-1002, 2003.
16. B. Taskar, P. Abbeel, and D. Koller. Discriminative probabilistic models for relational data. In Proc. of the 18th Conference on Uncertainty in Artificial Intelligence, pages 485-492, 2002.