50 years of data mining and OR: Upcoming trends and challenges

Journal of the Operational Research Society

Volumn 60, Issue SUPPL. 1, 2009, Pages

  Baesens, B ^a,b   Mues, C ^b   Martens, D ^a,c   Vanthienen, J ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

^c UNIVERSITY COLLEGE GHENT   (Belgium)

Author keywords

Applications; Data mining; Decision support systems; Learning algorithms; Prediction

Indexed keywords

APPLICATIONS; ARTIFICIAL INTELLIGENCE; DECISION MAKING; DECISION SUPPORT SYSTEMS; FORECASTING; GENETIC ALGORITHMS; LEARNING ALGORITHMS; LINEAR PROGRAMMING; QUADRATIC PROGRAMMING; RISK ASSESSMENT; TERRORISM;

ARTIFICIAL ANT COLONIES; COUNTER TERRORISM; DATA MINING METHODS; DOMAIN EXPERTS; FRAUD DETECTION; MONITOR MODELS; OPERATIONAL RESEARCH; QUADRATIC OPTIMIZATION;

DATA MINING;

EID: 64749091335     PISSN: 01605682     EISSN: 14769360     Source Type: Journal    
DOI: 10.1057/jors.2008.171     Document Type: Article

References (37)

1. Abbiw-Jackson R, Golden BL, Raghavan S and Wasil EA (2006). A divide-and-conquer local search heuristic for data visualization. Computers & OR 33: 3070-3087.
2. Baesens B, Egmont-Petersen M, Castelo R and Vanthienen J (2002). Learning bayesian network classifiers for credit scoring using Markov Chain Monte Carlo search. In: Sanfeliu A, Villanueva JJ, Vanrell M, Alquézar R, Jain AK and Kittler J (eds). Proceedings of the Sixteenth International Conference on Pattern Recognition (ICPR'2002). IEEE Computer Society: Québec, Canada, August, pp 49-52.
3. Baesens B, Setiono R, Mues C and Vanthienen J (2003a). Using neural network rule extraction and decision tables for credit-risk evaluation. Mngt Sci 49(3): 312-329.
4. Baesens B, Van Gestel T, Viaene S, Stepanova M, Suykens J and Vanthienen J (2003b). Benchmarking state-of-the-art classification algorithms for credit scoring. J opl Res Soc 54(6): 627-635.
5. Bennett KP (1992). Decision tree construction via linear programming. In: Evants M (ed). Proceedings of the Fourth Midwest Artificial Intelligence and Cognitive Science Conference. Utica, Illinois, Southern Illinois University: Illinois, pp 97-101.
6. Bradley PS, Mangasarian OL and Street WN (1997). Clustering via concave minimization. In: Mozer MC, Jordan MI and Petsche T (eds). Advances in Neural Information Processing Systems. MIT Press: Cambridge, MA.
7. Bradley PS, Fayyad UM and Mangasarian OL (1999). Mathematical programming for data mining: Formulations and challenges. Informs J Comput 11(3): 217-238.
8. Bredensteiner EJ and Bennett KP (1.998). Feature minimization within decision trees. Comput Optim Appl 10: 111-126.
9. Burges JC (1998). A Tutorial on support vector machines for pattern recognition. Data Mining Knowl Discov 2: 121-167.
10. Castermans G, Martens D, Van Gestel T, Hamers B, Baesens B (2007). An overview and framework for PD backtesting and benchmarking. In: Crook J, Thomas L and Hand DJ (eds.) Conference on Credit Scoring and Credit Control X, Edinburgh, UK, July. Edinburgh. University Press: Edinburgh.
11. De Moor B, Marchal K, Mathys .J and Moreau Y (2003). Bioinformatics: Organisms from Venus, technology from Jupiter, algorithms from Mars. Eur J Control 9: 237-278.
12. Di Caro G and Dorigo M (1998). Antnet: Distributed stigmergetic control for communications networks. J Artif Intel Res 9: 317-365.
13. Dorigo M and Stützle T (2004). Ant Colony Optimization. MIT Press: Cambridge, MA.
14. Durand D (1941). Risk Elements in Consumer Instalment Financing. National Bureau of Economic Research: New York.
15. Fildes R, Nikolopoulos K, Crone SF and Syntetos AA (2008). Forecasting and operational research: A review. J Opl Res Soc 59: 1150-1172.
16. Freitas AA (2007). A review of evolutionary algorithms for data mining. In: Maimon O and Rokach L (eds). Soft Computing for Knowledge Discovery and Data Mining. Springer: Berlin, pp 61-93.
17. Giudici P and Castelo R (2003). Improving Markov Chain Monte Carlo model search for data mining. Mach Learn 50(1-2): 127-158.
18. Holte RC (1993). Very simple classification rules perform well on most commonly used datasets. Mach Learn 11(1): 63-90.
19. Lee W, Stolfo SJ, Chan PK, Eskin E, Wei F, Miller M, Hershkop S, Junxin Z (2001). Real time data mining-based intrusion detection. In: DARPA Information Survivability Conference & Exposition II, DISCEX '01: Anaheim, CA, pp 89-100.
20. Lessmann S, Baesens B, Mues C and Pietsch S (2008). Benchmarking classification models for software defect prediction: a proposed framework, and novel findings. IEEE Trans Software Eng 34: 485-496.
21. Liu B (2006). Web Data Mining: Exploring Hyperlinks, Content and Usage Data. Springer: Berlin.
22. Lu Q and Getoor L (2003). Link-based classification. In: Fawcett T and Mishra N (eds.) Proceeding of the Twentieth Conference on Machine Learning (ICML-2003). AAAI Press: Washington, DC.
23. Macskassy SA and Provost F (2007). Classification in networked data: a toolkit and a univariate case study. J Mach Learn Res 8: 935-983.
24. Mangasarian OL (1965). Linear and nonlinear separation of patterns by linear programming. Opns Res 13: 455-461.
25. Mangasarian OL, Street WN and Wolberg WH (1995). Breast cancer diagnosis and prognosis via linear programming. Opns Res 43(4): 570-577.
26. Martens D and Baesens B (2009). Building acceptable classification models. Ann Inform Syst (forthcoming).
27. Martens D, De Backer M, Haesen R, Baesens B, Mues C, Vanthienen J (2006). Ant-based approach to the knowledge fusion problem. In: Dorigo M, Gambardella L, Birattari M, Martinoli A, Poli R and Stützte T (eds.) Lecture Notes in Computer Science, ANTS Workshop 2006, Brussels, Belgium, pp 84-95, September. Springer: Berlin.
28. Martens D, Baesens B, Van Gestel T and Vanthienen J (2007a). Comprehensible credit scoring models using rule extraction from support vector machines. Eur J Opl Res 183(3): 1466-1476.
29. Martens D, De Backer M, Haesen R, Vanthienen J, Snoeck M and Baesens B (2007b). Classification with. Ant Colony Optimization. IEEE Trans Evolut Comput 11(5): 651-665.
30. Olafsson S (2006). Introduction to operations research and data mining. Comput Opns Res 33(11): 3067-3069.
31. Padmanabhan B and Tuzhilin A (2003). On the use of optimization for data mining theoretical interactions and eCRM opportunities. Mngt Sci 49(10): 1327-1343.
32. Seow HY and Thomas LC (2007). To ask or not to ask: that is the question. Eur J Opl Res 183: 1513-1520.
33. Thomas LC, Edelman DB and Crook JN (2002). Credit Scoring and Its Applications. Society for Industrial Mathematics: Philadelphia, PA.
34. Van der Aalst WMP and Weijters AJMM (2004). Process mining: a research agenda. Comput Ind 53(3): 231-244.
35. Van Gestel T, Baesens B, Van Dijcke P, Suykens J, Garcia J and Alderweireld T (2005). Linear and nonlinear credit scoring by combining logistic regression and support vector machines. J Credit Risk 1(4).
36. Witten IH and Frank E (2000). Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann Publishers Inc.: San Francisco, CA.
37. Yang J and Olafsson S (2005). Optimization based feature selection with adaptive instance sampling. Comput Opns Res 33(11): 3088-3106.