Pareto-optimality of oblique decision trees from evolutionary algorithms

Journal of Global Optimization

Volumn 51, Issue 2, 2011, Pages 301-311

  Pangilinan, Jose Maria ^a   Janssens, Gerrit K ^b  

^a SAINT LOUIS UNIVERSITY   (Philippines)

^b HASSELT UNIVERSITY   (Belgium)

Author keywords

Classification; Data mining; Decision tree; Evolutionary algorithm; Multiobjective optimization; Pareto optimality

Indexed keywords

BENCHMARK DATASETS; CLASSIFICATION ACCURACY; DATA SETS; DECISION TREE CLASSIFIERS; LARGE DATASETS; MULTI OBJECTIVE; MULTI OBJECTIVE EVOLUTIONARY ALGORITHMS; OBLIQUE DECISION TREE; PARETO-OPTIMAL SETS; PARETO-OPTIMALITY; SOLUTION SET; TREE SIZE;

CLASSIFICATION (OF INFORMATION); DATA MINING; DECISION TREES; MULTIOBJECTIVE OPTIMIZATION; PARETO PRINCIPLE; PLANT EXTRACTS; TREES (MATHEMATICS);

EVOLUTIONARY ALGORITHMS;

EID: 80053299794     PISSN: 09255001     EISSN: 15732916     Source Type: Journal    
DOI: 10.1007/s10898-010-9614-9     Document Type: Article

References (39)

1. R. Quinlan 1986 Induction of decision trees Mach. Learn. 1 81 106
2. W. Loh N. Vanichsetakul 1988 Tree structured classification via generalized discriminant analysis J. Am. Stat. Assoc. 83 715 725 10.2307/2289295
3. W. Loh Y. Shih 1997 Split selection methods for classification trees Stat. Sin. 7 815 840
4. Breiman L., Friedman J., Olshen R., Stone C.: Classification and Regression Trees. Wadsworth, Belmont (1984)
5. Heath, D., Kasif, S., Salzberg, S.: Induction of oblique decision trees. Proceedings of IJCAI, Morgan Kaufman, Chambery, pp. 1002-1007 (1993)
6. S. Murthy S. Kasif S. Salzberg 1994 A system for induction of oblique decision trees J. Artif. Intel. Res. 2 1 32
7. S. Vadera 2005 Safer oblique trees without costs Expert Syst. 22 206 221 10.1111/j.1468-0394.2005.00311.x (Pubitemid 43906931)
8. N. Street 2005 Oblique multi-category decision trees using nonlinear programming J. Comput. 17 25 31
9. B. Moret 1982 Decision trees and diagrams Comput. Surv. 14 4 593 623 10.1145/356893.356898
10. Fayyad, U., Irani, K.: What should be minimized in a decision tree? Proceedings of the National Conference on Artificial Intelligence, Boston, pp. 749-754 (1990)
11. I. Kononenko I. Bratko 1991 Information based evaluation criterion for classifier's performance Mach. Learn. 6 67 80 (Pubitemid 21727226)
12. S. Gelfand C. Ravishankar E. Delp 1991 An iterative growing and pruning algorithm for classification tree design IEEE Trans. Pattern Anal. Mach. Intel. 13 163 174 10.1109/34.67645
13. Q. Wang C. Suen 1984 Analysis and design of a decision tree based on entropy reduction and its application to large character set recognition IEEE Trans. Pattern Anal. Mach. Intel. 6 406 417 10.1109/TPAMI.1984.4767546 (Pubitemid 14610502)
14. S. Murthy 1998 Automatic construction of decision trees from data: a multi-disciplinary survey Data Min. Knowl. Discov. 2 345 398 10.1023/A:1009744630224
15. Murthy, S., Salzberg, S.: Look-ahead and pathology in decision tree induction. Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal, pp. 1025-1033 (1995)
16. Koza, J.: Hierarchical genetic algorithms operating in populations of computer programs. Proceedings of the 11th International Joint Conference on Artificial Intelligence, pp. 768-774 (1989)
17. Lutsko J.F., Kuijpers B.: Simulated annealing in the construction of near-optimal decision trees. In: Chessman, P., Oldford, R.W. (eds) Selecting Models from Data: AI and Stat. IV, pp. 453-462. Springer, Berlin (1994)
18. Folino, G., Pizzuti, C., Spezzano, G., Spezzano, O.: Genetic programming and simulated annealing: a hybrid method to evolve decision trees. Proceedings of the European Conference on Genetic Programming, Edinburgh, pp. 294-303 (2000)
19. Kwok, S., Carter, C.: Multiple decision trees. Proceedings of the 4th Annual Conference on Uncertainty in Artificial Intelligence, Cambridge, pp. 327-325 (1990)
20. S. Shlien 1992 Nonparametric classification using matched binary decision trees Pattern Recognit. Lett. 13 83 88 10.1016/0167-8655(92)90037-Z
21. W. Buntine 1992 Learning classification trees Stat. Comput. 2 63 73 10.1007/BF01889584
22. Heath, D., Kasif, S., Salzberg, S.: k-DT: A multi-tree learning method. Proceedings of the 2nd International Workshop on Multi-strategy Learning, West Virginia, pp. 138-149 (1993)
23. E. Cantu-Paz C. Kamath 2003 Inducing oblique decision trees with evolutionary algorithms IEEE Trans. Evol. Comput. 7 54 68 10.1109/TEVC.2002. 806857
24. G. Harik E. Cantu-Paz D. Goldberg B. Miller 1999 The gamblers ruin problem, genetic algorithms, and the sizing of populations Evol. Comput. 7 231 253 10.1162/evco.1999.7.3.231
25. B. Miller D. Goldberg 1996 Genetic algorithm selections schemes, and the varying effects of noise Evol. Comput. 4 113 131 10.1162/evco.1996.4.2.113 (Pubitemid 126706153)
26. K. Sörensen G.K. Janssens 2003 Data mining with genetic algorithms on binary trees Eur. J. Oper. Res. 151 253 264 10.1016/S0377-2217(02)00824-X
27. Papagelis, A., Kalles, D.: Breeding decision trees using evolutionary techniques. Proceedings of the 8th International Conference on Machine Learning, Williamstown, pp. 393-400 (2001)
28. Bot, M., Langdon, W.: Application of genetic programming to induction of linear classification trees. Proceedings of European Conference on Genetic Programming, pp. 247-258. Springer, Edinburgh (2000)
29. Kim, D.: Structural risk minimization on decision trees using evolutionary multiobjective optimization. Springer Lecture Notes on Computer Science 3003, pp. 338-348 (2004)
30. N. Bryson 2004 Evaluation of decision trees: a multicriteria approach Comp. Oper. Res. 31 1933 1945 10.1016/S0305-0548(03)00156-4 (Pubitemid 38527454)
31. H. Zhao 2007 A multiobjective genetic programming approach to developing Pareto-optimal decision trees Decis. Support Syst. 43 809 826 10.1016/j.dss.2006.12.011 (Pubitemid 46466991)
32. M. Doumpos E. Chatzi C. Zopounidis 2006 An experimental evaluation of some classification methods J. Glob. Optim. 36 2 33 50 10.1007/s10898-005-6152-y (Pubitemid 44230324)
33. A. Chinchuluun P.M. Pardalos 2007 A survey of recent developments in multiobjective optimization Ann. Oper. Res. 154 1 29 50 10.1007/s10479-007-0186- 0
34. Pappalardo M.: Multiobjective optimization: a brief overview. In: Chichuluun, A., Pardalos, P.M., Migdalas, A., Pitsoulis, L. (eds) Pareto Optimality, Game Theory and Equilibria, pp. 517-528. Springer, Berlin (2008)
35. E. Zitzler L. Thiele 2000 Comparison of multi-objective evolutionary algorithms: empirical results Evol. Comput. 8 125 148 10.1162/106365600568202
36. Zitzler E., Laumanns M., Thiele L.: SPEA2: improving the strength Pareto evolutionary algorithm for multiobjective optimization. In: Giannakoglou, K., Tsahalis, D., Periaux, J., Papailu, K., Fogarty, T. (eds) Evolutionary Methods for Design, Optimization, and Control, pp. 19-26. CIMNE, Barcelona (2002)
37. K. Deb S. Agrawal A. Pratap T. Meyarivan 2002 A fast elitist multiobjective genetic algorithm: NSGA-II IEEE Trans. Evol. Comput. 6 182 197 10.1109/4235.996017 (Pubitemid 34555372)
38. C. Gil A. Márquez R. Baños M.G. Montoya J. Gómez 2007 A hybrid method for solving multi-objective global optimization problems J. Glob. Optim. 38 2 265 281 10.1007/s10898-006-9105-1 (Pubitemid 46686132)
39. Deb K.: Multiobjective Optimization Using Evolutionary Algorithms. Wiley, UK (2001)