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Abstract

Classification, which is the data mining task of assigning objects to predefined categories, is widely used in the process of intelligent decision making.

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References

  1. R.C. Barros et al., A bottom-up oblique decision tree induction algorithm, in 11th International Conference on Intelligent Systems Design and Applications. pp. 450–456 (2011)

    Google Scholar 

  2. R.C. Barros et al., A survey of evolutionary algorithms for decision-tree induction. IEEE Trans. Syst., Man, Cybern., Part C: Appl. Rev. 42(3), 291–312 (2012)

    Article  MathSciNet  Google Scholar 

  3. K. Bennett, O. Mangasarian, Multicategory discrimination via linear programming. Optim. Methods Softw. 2, 29–39 (1994)

    Google Scholar 

  4. L. Breiman et al., Classification and Regression Trees (Wadsworth, Belmont, 1984)

    MATH  Google Scholar 

  5. L. Breiman, Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  6. L. Breslow, D. Aha, Simplifying decision trees: a survey. Knowl. Eng. Rev. 12(01), 1–40 (1997)

    Article  Google Scholar 

  7. P. Cowling, G. Kendall, E. Soubeiga, A Hyperheuristic Approach to Scheduling a Sales Summit, in Practice and Theory of Automated Timetabling III, Vol. 2079. Lecture Notes in Computer Science, ed. by E. Burke, W. Erben (Springer, Berlin, 2001), pp. 176–190

    Chapter  Google Scholar 

  8. A.E. Eiben, J.E. Smith, Introduction to Evolutionary Computing (Natural Computing Series) (Springer, Berlin, 2008)

    Google Scholar 

  9. F. Esposito, D. Malerba, G. Semeraro, A comparative analysis of methods for pruning decision trees. IEEE Trans. Pattern Anal. Mach. Intell. 19(5), 476–491 (1997)

    Article  Google Scholar 

  10. A.A. Freitas, Data Mining and Knowledge Discovery with Evolutionary Algorithms (Springer, New York, 2002). ISBN: 3540433317

    Book  MATH  Google Scholar 

  11. A.A. Freitas, A Review of evolutionary Algorithms for Data Mining, in Soft Computing for Knowledge Discovery and Data Mining, ed. by O. Maimon, L. Rokach (Springer, Berlin, 2008), pp. 79–111. ISBN: 978-0-387-69935-6

    Chapter  Google Scholar 

  12. A.A. Freitas, D.C. Wieser, R. Apweiler, On the importance of comprehensible classification models for protein function prediction. IEEE/ACM Trans. Comput. Biol. Bioinform. 7, 172–182 (2010). ISSN: 1545–5963

    Google Scholar 

  13. KDNuggets, Poll: Data mining/analytic methods you used frequently in the past 12 months (2007)

    Google Scholar 

  14. A. Keane, S. Brown, The design of a satellite boom with enhanced vibration performance using genetic algorithm techniques, in Conference on Adaptative Computing in Engineering Design and Control. Plymouth, pp. 107–113 (1996)

    Google Scholar 

  15. B. Kim, D. Landgrebe, Hierarchical classifier design in high-dimensional numerous class cases. IEEE Trans. Geosci. Remote Sens. 29(4), 518–528 (1991)

    Article  Google Scholar 

  16. J.R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection (MIT Press, Cambridge, 1992). ISBN: 0-262-11170-5

    MATH  Google Scholar 

  17. G. Landeweerd et al., Binary tree versus single level tree classification of white blood cells. Pattern Recognit. 16(6), 571–577 (1983)

    Article  Google Scholar 

  18. A.R. Oganov et al., Ionic high-pressure form of elemental boron. Nature 457, 863–867 (2009)

    Article  Google Scholar 

  19. G.L. Pappa et al., Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms, in Genetic Programming and Evolvable Machines (2013)

    Google Scholar 

  20. G.L. Pappa, A.A. Freitas, Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach (Springer Publishing Company Incorporated, New York, 2009)

    Google Scholar 

  21. J.R. Quinlan, C4.5: Programs for Machine Learning (Morgan Kaufmann, San Francisco, 1993). ISBN: 1-55860-238-0

    Google Scholar 

  22. L. Rokach, O. Maimon, Top-down induction of decision trees classifiers—a survey. IEEE Trans. Syst. Man, Cybern. Part C: Appl. Rev. 35(4), 476–487 (2005)

    Article  Google Scholar 

  23. K.A. Smith-Miles, Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Comput. Surv. 41, 6:1–6:25 (2009)

    Google Scholar 

  24. K.O. Stanley, R. Miikkulainen, Evolving neural networks through augmenting topologies. Evol. Comput. 10(2), 99–127 (2002). ISSN: 1063–6560

    Article  Google Scholar 

  25. A. Vella, D. Corne, C. Murphy, Hyper-heuristic decision tree induction, in World Congress on Nature and Biologically Inspired Computing, pp. 409–414 (2010)

    Google Scholar 

  26. D.H. Wolpert, W.G. Macready, No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)

    Article  Google Scholar 

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Correspondence to Rodrigo C. Barros .

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Barros, R.C., de Carvalho, A.C.P.L.F., Freitas, A.A. (2015). Introduction. In: Automatic Design of Decision-Tree Induction Algorithms. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-14231-9_1

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  • DOI: https://doi.org/10.1007/978-3-319-14231-9_1

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