A novel approach to cutting decision trees

Central European Journal of Operations Research

Volumn 22, Issue 3, 2014, Pages 553-565

  Üney Yüksektepe, Fadime ^a  

^a ISTANBUL KULTUR UNIVERSITY   (Turkey)

Author keywords

Data mining; Decision trees; Discriminant analysis; Mathematical programming; Piecewise linear models

Indexed keywords

EID: 84904416873     PISSN: 1435246X     EISSN: 16139178     Source Type: Journal    
DOI: 10.1007/s10100-013-0312-9     Document Type: Article

References (28)

1. Abad PL, Banks WL (1993) New LP based heuristics for the classification problem. Eur J Oper Res 67: 88-100.
2. Armutlu P, Ozdemir ME, Uney-Yuksektepe F, Kavakli IH, Turkay M (2008) Classification of drug molecules considering their IC50 values using mixed-integer linear programming based hyper-boxes method. BMC Bioinformatics 9: 411-424.
3. Bennett KP, Blue JA (1998) A support vector machine approach to decision trees. In: Blue JA (ed) Proceedings of the IEEE World Congress on computational intelligence. Anchorage, USA, pp 2396-2401.
4. Better M, Glover F, Samorani M (2010) Classification by vertical and cutting multi-hyperplane decision tree induction. Decis Support Syst 48: 430-436.
5. Brodley CE, Utgoff PE (1995) Multivariate decision trees. Mach Learn 19: 45-77.
6. Dagliyan O, Uney-Yuksektepe F, Kavakli IH, Turkay M (2011) Optimization based tumor classification from micro array gene expression data. PLoS ONE 6(2): e14579.
7. Erenguc SS, Koehler GJ (1990) Survey of mathematical programming models and experimental results for linear discriminant analysis. Manag Decis Econ 11: 215-225.
8. Freed N, Glover F (1981) Simple but powerful goal programming models for discriminant problems. Eur J Oper Res 7: 44-60.
9. Glen JJ (1999) Integer programming methods for normalisation and variable selection in mathematical programming discriminant analysis models. J Oper Res Soc 50: 1043-1053.
10. Glen JJ (2003) An iterative mixed integer programming method for classification accuracy maximizing discriminant analysis. Comput Oper Res 30: 181-198.
11. Glen JJ (2004) Mathematical programming models for piecewise-linear discriminant analysis. J Oper Res Soc 56: 331-341.
12. Glover F (1990) Improved linear programming models for discriminant analysis. Decis Sci 21: 771-785.
13. Guidici P, Figini S (2009) Applied data mining for business and industry. Wiley, UK.
14. Han J, Kamber M (2006) Data mining concepts and techniques. Elsevier Inc., UK.
15. Li T, Zhu S, Ogihara M (2006) Using discriminant analysis for multi-class classification: an experimental investigation. Knowl Inf Syst 10(4): 453-472.
16. Mitchell T (1997) Machine learning. Mc-Graw Hill, USA.
17. Roiger RJ, Geatz MW (2003) Data mining a tutorial-based primer. Pearson Education, Boston.
18. Rokach L, Maimon O (2005) Top-down induction of decision trees classifiers-a survey. IEEE Trans Syst Man Cybern Part C: Appl Rev 35: 476-487.
19. Stam A, Ragsdale CT (1992) On the classification gap in mathematical programming-based approaches to the disciriminant problem. Nav Res Logist 39: 545-559.
20. Sueyoshi T (2001) Extended DEA-discriminant analysis. Eur J Oper Res 131: 324-351.
21. Sueyoshi T, Goto M (2009) Methodological comparison between DEA (Data Envelopment Analysis) and DEA-DA (Discriminant Analysis) from the perspective of bankruptcy assessment. Eur J Oper Res 199: 561-575.
22. Tan PN, Steinbach M, Kumar V (2006) Introduction to data mining. Pearson Education, Boston.
23. Uney F, Turkay M (2006) A mixed-integer programming approach to multi-class data classification problem. Eur J Oper Res 173(3): 910-920.
24. Uney-Yuksektepe F, Yilmaz O, Turkay M (2008) Prediction of secondary structures of proteins using a two-stage method. Comput Chem Eng 32(1-2): 78-88.
25. Webb A (2002) Statistical pattern recognition. Wiley, UK.
26. Weiss SM, Kulikowski CA (1991) Computer systems that learn: classification and prediction methods from statistics, neural networks, machine learning and expert systems. Morgan Kaufmann, San Mateo.
27. Witten IH, Frank E (2005) Data mining practical machine learning tools and techniques. Elsevier Inc., USA.
28. Yeh IC, Yang KJ, Ting TM (2008) Knowledge discovery on RFM model using Bernoulli sequence. Expert Syst Appl. doi: 10. 1016/j. eswa. 2008. 07. 018.