Decision-Tree Induction

  • Rodrigo C. BarrosEmail author
  • André C. P. L. F. de Carvalho
  • Alex A. Freitas
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)


Decision-tree induction algorithms are highly used in a variety of domains for knowledge discovery and pattern recognition. They have the advantage of producing a comprehensible classification/regression model and satisfactory accuracy levels in several application domains, such as medical diagnosis and credit risk assessment. In this chapter, we present in detail the most common approach for decision-tree induction: top-down induction (Sect. 2.3). Furthermore, we briefly comment on some alternative strategies for induction of decision trees (Sect. 2.4). Our goal is to summarize the main design options one has to face when building decision-tree induction algorithms. These design choices will be specially interesting when designing an evolutionary algorithm for evolving decision-tree induction algorithms.


Decision trees Hunt’s algorithm Top-down induction Design components 


  1. 1.
    A. Agresti, Categorical Data Analysis, 2nd edn., Wiley Series in Probability and Statistics (Wiley-Interscience, Hoboken, 2002)CrossRefzbMATHGoogle Scholar
  2. 2.
    E. Alpaydin, Introduction to Machine Learning (MIT Press, Cambridge, 2010). ISBN: 026201243X, 9780262012430zbMATHGoogle Scholar
  3. 3.
    P.W. Baim, A method for attribute selection in inductive learning systems. IEEE Trans. Pattern Anal. Mach. Intell. 10(6), 888–896 (1988)CrossRefGoogle Scholar
  4. 4.
    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
  5. 5.
    R.C. Barros et al., A framework for bottom-up induction of decision trees, Neurocomputing (2013 in press)Google Scholar
  6. 6.
    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)CrossRefMathSciNetGoogle Scholar
  7. 7.
    M.P. Basgalupp et al., A beam-search based decision-tree induction algorithm, in Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques. IGI-Global (2011)Google Scholar
  8. 8.
    K. Bennett, Global tree optimization: a non-greedy decision tree algorithm. Comput. Sci. Stat. 26, 156–160 (1994)Google Scholar
  9. 9.
    K. Bennett, O. Mangasarian, Multicategory discrimination via linear programming. Optim. Methods Softw. 2, 29–39 (1994)Google Scholar
  10. 10.
    K. Bennett, O. Mangasarian, Robust linear programming discrimination of two linearly inseparable sets. Optim. Methods Softw. 1, 23–34 (1992)CrossRefGoogle Scholar
  11. 11.
    L. Bobrowski, M. Kretowski, Induction of multivariate decision trees by using dipolar criteria, in European Conference on Principles of Data Mining and Knowledge Discovery. pp. 331–336 (2000)Google Scholar
  12. 12.
    L. Breiman et al., Classification and Regression Trees (Wadsworth, Belmont, 1984)zbMATHGoogle Scholar
  13. 13.
    L. Breslow, D. Aha, Simplifying decision trees: a survey. Knowl. Eng. Rev. 12(01), 1–40 (1997)CrossRefGoogle Scholar
  14. 14.
    C.E. Brodley, P.E. Utgoff, Multivariate versus univariate decision trees. Technical Report. Department of Computer Science, University of Massachusetts at Amherst (1992)Google Scholar
  15. 15.
    A. Buja, Y.-S. Lee, Data mining criteria for tree-based regression and classification, in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 27–36 (2001)Google Scholar
  16. 16.
    W. Buntine, A theory of learning classification rules, PhD thesis. University of Technology, Sydney (1992)Google Scholar
  17. 17.
    W. Buntine, Learning classification trees. Stat. Comput. 2, 63–73 (1992)CrossRefGoogle Scholar
  18. 18.
    R. Casey, G. Nagy, Decision tree design using a probabilistic model. IEEE Trans. Inf. Theory 30(1), 93–99 (1984)CrossRefGoogle Scholar
  19. 19.
    B. Cestnik, I. Bratko, On estimating probabilities in tree pruning, Machine Learning-EWSL-91, Vol. 482. Lecture Notes in Computer Science (Springer, Berlin, 1991), pp. 138–150CrossRefGoogle Scholar
  20. 20.
    B. Chandra, R. Kothari, P. Paul, A new node splitting measure for decision tree construction. Pattern Recognit. 43(8), 2725–2731 (2010)CrossRefzbMATHGoogle Scholar
  21. 21.
    B. Chandra, P.P. Varghese, Moving towards efficient decision tree construction. Inf. Sci. 179(8), 1059–1069 (2009)CrossRefzbMATHGoogle Scholar
  22. 22.
    J. Ching, A. Wong, K. Chan, Class-dependent discretization for inductive learning from continuous and mixed-mode data. IEEE Trans. Pattern Anal. Mach. Intell. 17(7), 641–651 (1995)CrossRefGoogle Scholar
  23. 23.
    P. Chou, Optimal partitioning for classification and regression trees. IEEE Trans. Pattern Anal. Mach. Intell. 13(4), 340–354 (1991)CrossRefGoogle Scholar
  24. 24.
    P. Clark, T. Niblett, The CN2 induction algorithm. Mach. Learn. 3(4), 261–283 (1989)Google Scholar
  25. 25.
    D. Coppersmith, S.J. Hong, J.R.M. Hosking, Partitioning nominal attributes in decision trees. Data Min. Knowl. Discov. 3, 197–217 (1999)CrossRefGoogle Scholar
  26. 26.
    R.L. De Mántaras, A distance-based attribute selection measure for decision tree induction. Mach. Learn. 6(1), 81–92 (1991). ISSN: 0885–6125CrossRefGoogle Scholar
  27. 27.
    G. De’ath, Multivariate regression trees: A new technique for modeling species-environment relationships. Ecology 83(4), 1105–1117 (2002)Google Scholar
  28. 28.
    L. Devroye, L. Györfi, G. Lugosi, A Probabilistic Theory of Pattern Recognition (Springer, New York, 1996)CrossRefzbMATHGoogle Scholar
  29. 29.
    M. Dong, R. Kothari, Look-ahead based fuzzy decision tree induction. IEEE Trans. Fuzzy Syst. 9(3), 461–468 (2001)CrossRefGoogle Scholar
  30. 30.
    B. Draper, C. Brodley, Goal-directed classification using linear machine decision trees. IEEE Trans. Pattern Anal. Mach. Intell. 16(9), 888–893 (1994)CrossRefGoogle Scholar
  31. 31.
    S. Esmeir, S. Markovitch, Anytime learning of decision trees. J. Mach. Learn. Res. 8, 891–933 (2007)zbMATHGoogle Scholar
  32. 32.
    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)CrossRefGoogle Scholar
  33. 33.
    F. Esposito, D. Malerba, G. Semeraro, A further study of pruning methods in decision tree induction, in Fifth International Workshop on Artificial Intelligence and Statistics. pp. 211–218 (1995)Google Scholar
  34. 34.
    F. Esposito, D. Malerba, G. Semeraro, Simplifying decision trees by pruning and grafting: new results (extended abstract), in 8th European Conference on Machine Learning. ECML’95. (Springer, London, 1995) pp. 287–290Google Scholar
  35. 35.
    U. Fayyad, K. Irani, The attribute selection problem in decision tree generation, in National Conference on Artificial Intelligence. pp. 104–110 (1992)Google Scholar
  36. 36.
    A. Frank, A. Asuncion, UCI Machine Learning Repository (2010)Google Scholar
  37. 37.
    A.A. Freitas, A critical review of multi-objective optimization in data mining: a position paper. SIGKDD Explor. Newsl. 6(2), 77–86 (2004). ISSN: 1931–0145CrossRefMathSciNetGoogle Scholar
  38. 38.
    J.H. Friedman, A recursive partitioning decision rule for nonparametric classification. IEEE Trans. Comput. 100(4), 404–408 (1977)CrossRefGoogle Scholar
  39. 39.
    S.B. Gelfand, C.S. Ravishankar, E.J. Delp, An iterative growing and pruning algorithm for classification tree design. IEEE Int. Conf. Syst. Man Cybern. 2, 818–823 (1989)CrossRefGoogle Scholar
  40. 40.
    M.W. Gillo, MAID: A Honeywell 600 program for an automatised survey analysis. Behav. Sci. 17, 251–252 (1972)Google Scholar
  41. 41.
    M. Gleser, M. Collen, Towards automated medical decisions. Comput. Biomed. Res. 5(2), 180–189 (1972)CrossRefGoogle Scholar
  42. 42.
    L.A. Goodman, W.H. Kruskal, Measures of association for cross classifications. J. Am. Stat. Assoc. 49(268), 732–764 (1954)zbMATHGoogle Scholar
  43. 43.
    T. Hancock et al., Lower bounds on learning decision lists and trees. Inf. Comput. 126(2) (1996)Google Scholar
  44. 44.
    C. Hartmann et al., Application of information theory to the construction of efficient decision trees. IEEE Trans. Inf. Theory 28(4), 565–577 (1982)CrossRefzbMATHGoogle Scholar
  45. 45.
    R. Haskell, A. Noui-Mehidi, Design of hierarchical classifiers, in Computing in the 90s, Vol. 507. Lecture Notes in Computer Science, ed. by N. Sherwani, E. de Doncker, J. Kapenga (Springer, Berlin, 1991), pp. 118–124CrossRefGoogle Scholar
  46. 46.
    H. Hauska, P. Swain, The decision tree classifier: design and potential, in 2nd Symposium on Machine Processing of Remotely Sensed Data (1975)Google Scholar
  47. 47.
    D. Heath, S. Kasif, S. Salzberg, Induction of oblique decision trees. J. Artif. Intell. Res. 2, 1–32 (1993)Google Scholar
  48. 48.
    W. Hsiao, Y. Shih, Splitting variable selection for multivariate regression trees. Stat. Probab. Lett. 77(3), 265–271 (2007)CrossRefzbMATHMathSciNetGoogle Scholar
  49. 49.
    E.B. Hunt, J. Marin, P.J. Stone, Experiments in Induction (Academic Press, New York, 1966)Google Scholar
  50. 50.
    L. Hyafil, R. Rivest, Constructing optimal binary decision trees is NP-complete. Inf. Process. Lett. 5(1), 15–17 (1976)CrossRefzbMATHMathSciNetGoogle Scholar
  51. 51.
    A. Ittner, Non-linear decision trees, in 13th International Conference on Machine Learning. pp. 1–6 (1996)Google Scholar
  52. 52.
    B. Jun et al., A new criterion in selection and discretization of attributes for the generation of decision trees. IEEE Trans. Pattern Anal. Mach. Intell. 19(2), 1371–1375 (1997)CrossRefGoogle Scholar
  53. 53.
    G. Kalkanis, The application of confidence interval error analysis to the design of decision tree classifiers. Pattern Recognit. Lett. 14(5), 355–361 (1993)CrossRefGoogle Scholar
  54. 54.
    A. Karali\(\check{c}\), Employing linear regression in regression tree leaves, 10th European Conference on Artificial Intelligence. ECAI’92 (Wiley, New York, 1992)Google Scholar
  55. 55.
    G.V. Kass, An exploratory technique for investigating large quantities of categorical data. APPL STATIST 29(2), 119–127 (1980)CrossRefGoogle Scholar
  56. 56.
    B. Kim, D. Landgrebe, Hierarchical classifier design in high-dimensional numerous class cases. IEEE Trans. Geosci. Remote Sens. 29(4), 518–528 (1991)CrossRefGoogle Scholar
  57. 57.
    I. Kononenko, I. Bratko, E. Roskar, Experiments in automatic learning of medical diagnostic rules. Technical Report Ljubljana, Yugoslavia: Jozef Stefan Institute (1984)Google Scholar
  58. 58.
    I. Kononenko, Estimating attributes: analysis and extensions of RELIEF, Proceedings of the European Conference on Machine Learning on Machine Learning (Springer, New York, 1994). ISBN: 3-540-57868-4Google Scholar
  59. 59.
    G. Landeweerd et al., Binary tree versus single level tree classification of white blood cells. Pattern Recognit. 16(6), 571–577 (1983)CrossRefGoogle Scholar
  60. 60.
    D.R. Larsen, P.L. Speckman, Multivariate regression trees for analysis of abundance data. Biometrics 60(2), 543–549 (2004)CrossRefzbMATHMathSciNetGoogle Scholar
  61. 61.
    Y.-S. Lee, A new splitting approach for regression trees. Technical Report. Dongguk University, Department of Statistics: Dongguk University, Department of Statistics (2001)Google Scholar
  62. 62.
    X. Li, R.C. Dubes, Tree classifier design with a permutation statistic. Pattern Recognit. 19(3), 229–235 (1986)CrossRefGoogle Scholar
  63. 63.
    X.-B. Li et al., Multivariate decision trees using linear discriminants and tabu search. IEEE Trans. Syst., Man, Cybern.-Part A: Syst. Hum. 33(2), 194–205 (2003)CrossRefGoogle Scholar
  64. 64.
    H. Liu, R. Setiono, Feature transformation and multivariate decision tree induction. Discov. Sci. 1532, 279–291 (1998)Google Scholar
  65. 65.
    W. Loh, Y. Shih, Split selection methods for classification trees. Stat. Sin. 7, 815–840 (1997)zbMATHMathSciNetGoogle Scholar
  66. 66.
    W. Loh, Regression trees with unbiased variable selection and interaction detection. Stat. Sin. 12, 361–386 (2002)zbMATHMathSciNetGoogle Scholar
  67. 67.
    D. Malerba et al., Top-down induction of model trees with regression and splitting nodes. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 612–625 (2004)CrossRefGoogle Scholar
  68. 68.
    O. Mangasarian, R. Setiono, W. H. Wolberg, Pattern recognition via linear programming: theory and application to medical diagnosis, in SIAM Workshop on Optimization (1990)Google Scholar
  69. 69.
    N. Manwani, P. Sastry, A Geometric Algorithm for Learning Oblique Decision Trees, in Pattern Recognition and Machine Intelligence, ed. by S. Chaudhury, et al. (Springer, Berlin, 2009), pp. 25–31CrossRefGoogle Scholar
  70. 70.
    J. Martin, An exact probability metric for decision tree splitting and stopping. Mach. Learn. 28(2), 257–291 (1997)CrossRefGoogle Scholar
  71. 71.
    J. Mingers, An empirical comparison of pruning methods for decision tree induction. Mach. Learn. 4(2), 227–243 (1989)CrossRefGoogle Scholar
  72. 72.
    J. Mingers, An empirical comparison of selection measures for decision-tree induction. Mach. Learn. 3(4), 319–342 (1989)Google Scholar
  73. 73.
    J. Mingers, Expert systems—rule induction with statistical data. J. Oper. Res. Soc. 38, 39–47 (1987)Google Scholar
  74. 74.
    T.M. Mitchell, Machine Learning (McGraw-Hill, New York, 1997)zbMATHGoogle Scholar
  75. 75.
    F. Mola, R. Siciliano, A fast splitting procedure for classification trees. Stat. Comput. 7(3), 209–216 (1997)CrossRefGoogle Scholar
  76. 76.
    J.N. Morgan, R.C. Messenger, THAID: a sequential search program for the analysis of nominal scale dependent variables. Technical Report. Institute for Social Research, University of Michigan (1973)Google Scholar
  77. 77.
    S.K. Murthy, S. Kasif, S.S. Salzberg, A system for induction of oblique decision trees. J. Artif. Intell. Res. 2, 1–32 (1994)zbMATHGoogle Scholar
  78. 78.
    S.K. Murthy, Automatic construction of decision trees from data: A multi-disciplinary survey. Data Min. Knowl. Discov. 2(4), 345–389 (1998)CrossRefGoogle Scholar
  79. 79.
    S.K. Murthy, S. Salzberg, Lookahead and pathology in decision tree induction, in 14th International Joint Conference on Artificial Intelligence. (Morgan Kaufmann, San Francisco, 1995), pp. 1025–1031Google Scholar
  80. 80.
    S.K. Murthy et al., OC1: A randomized induction of oblique decision trees, in Proceedings of the 11th National Conference on Artificial Intelligence (AAAI’93), pp. 322–327 (1993)Google Scholar
  81. 81.
    G.E. Naumov, NP-completeness of problems of construction of optimal decision trees. Sov. Phys. Doklady 36(4), 270–271 (1991)zbMATHMathSciNetGoogle Scholar
  82. 82.
    T. Niblett, I. Bratko, Learning decision rules in noisy domains, in 6th Annual Technical Conference on Research and Development in Expert Systems III. pp. 25–34 (1986)Google Scholar
  83. 83.
    N.J. Nilsson, The Mathematical Foundations of Learning Machines (Morgan Kaufmann Publishers Inc., San Francisco, 1990). ISBN: 1-55860-123-6zbMATHGoogle Scholar
  84. 84.
    S.W. Norton, Generating better decision trees, 11th International Joint Conference on Artificial Intelligence (Morgan Kaufmann Publishers Inc., San Francisco, 1989)Google Scholar
  85. 85.
    K. Osei-Bryson, Post-pruning in regression tree induction: an integrated approach. Expert Syst. Appl. 34(2), 1481–1490 (2008)CrossRefGoogle Scholar
  86. 86.
    D. Page, S. Ray, Skewing: An efficient alternative to lookahead for decision tree induction, in 18th International Joint Conference on Artificial Intelligence (Morgan Kaufmann Publishers Inc., San Francisco, 2003), pp. 601–607Google Scholar
  87. 87.
    A. Patterson, T. Niblett, ACLS User Manual (Intelligent Terminals Ltd., Glasgow, 1983)Google Scholar
  88. 88.
    K. Pattipati, M. Alexandridis, Application of heuristic search and information theory to sequential fault diagnosis. IEEE Trans. Syst. Man Cybern. 20, 872–887 (1990)CrossRefzbMATHGoogle Scholar
  89. 89.
    J.R. Quinlan, C4.5: Programs for Machine Learning (Morgan Kaufmann, San Francisco, 1993). ISBN: 1-55860-238-0Google Scholar
  90. 90.
    J.R. Quinlan, Decision trees as probabilistic classifiers, in 4th International Workshop on Machine Learning (1987)Google Scholar
  91. 91.
    J.R. Quinlan, Discovering rules by induction from large collections of examples, in Expert Systems in the Micro-elect Age, ed. by D. Michie (Edinburgh University Press, Edinburgh, 1979)Google Scholar
  92. 92.
    J.R. Quinlan, Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)Google Scholar
  93. 93.
    J.R. Quinlan, Learning with continuous classes, in 5th Australian Joint Conference on Artificial Intelligent. 92, pp. 343–348 (1992)Google Scholar
  94. 94.
    J.R. Quinlan, Simplifying decision trees. Int. J. Man-Mach. Stud. 27, 221–234 (1987)CrossRefGoogle Scholar
  95. 95.
    J.R. Quinlan, Unknown attribute values in induction, in 6th International Workshop on Machine Learning. pp. 164–168 (1989)Google Scholar
  96. 96.
    J.R. Quinlan, R.L. Rivest, Inferring decision trees using the minimum description length principle. Inf. Comput. 80(3), 227–248 (1989)CrossRefzbMATHMathSciNetGoogle Scholar
  97. 97.
    M. Robnik-Sikonja, I. Kononenko, Pruning regression trees with MDL, in European Conference on Artificial Intelligence. pp. 455–459 (1998)Google Scholar
  98. 98.
    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)CrossRefGoogle Scholar
  99. 99.
    E.M. Rounds, A combined nonparametric approach to feature selection and binary decision tree design. Pattern Recognit. 12(5), 313–317 (1980)CrossRefGoogle Scholar
  100. 100.
    J.P. Sá et al., Decision trees using the minimum entropy-of-error principle, in 13th International Conference on Computer Analysis of Images and Patterns (Springer, Berlin, 2009), pp. 799–807Google Scholar
  101. 101.
    S. Safavian, D. Landgrebe, A survey of decision tree classifier methodology. IEEE Trans. Syst. Man Cybern. 21(3), 660–674 (1991). ISSN: 0018–9472CrossRefMathSciNetGoogle Scholar
  102. 102.
    I.K. Sethi, G.P.R. Sarvarayudu, Hierarchical classifier design using mutual information. IEEE Trans. Pattern Anal. Mach. Intell. 4(4), 441–445 (1982)CrossRefGoogle Scholar
  103. 103.
    S. Shah, P. Sastry, New algorithms for learning and pruning oblique decision trees. IEEE Trans. Syst. Man, Cybern. Part C: Applic. Rev. 29(4), 494–505 (1999)CrossRefGoogle Scholar
  104. 104.
    C.E. Shannon, A mathematical theory of communication. BELL Syst. Tech. J. 27(1), 379–423, 625–56 (1948)Google Scholar
  105. 105.
    Y. Shih, Selecting the best categorical split for classification trees. Stat. Probab. Lett. 54, 341–345 (2001)CrossRefzbMATHGoogle Scholar
  106. 106.
    L.M. Silva et al., Error entropy in classification problems: a univariate data analysis. Neural Comput. 18(9), 2036–2061 (2006)CrossRefzbMATHMathSciNetGoogle Scholar
  107. 107.
    J.A. Sonquist, E.L. Baker, J.N. Morgan, Searching for structure. Technical Report. Institute for Social Research University of Michigan (1971)Google Scholar
  108. 108.
    J. Talmon, A multiclass nonparametric partitioning algorithm. Pattern Recognit. Lett. 4(1), 31–38 (1986)CrossRefGoogle Scholar
  109. 109.
    P.J. Tan, D.L. Dowe, MML inference of oblique decision trees, in 17th Australian Joint Conference on AI. pp. 1082–1088 (2004)Google Scholar
  110. 110.
    P.-N. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining (Addison-Wesley, Boston, 2005)Google Scholar
  111. 111.
    P.C. Taylor, B.W. Silverman, Block diagrams and splitting criteria for classification trees. Stat. Comput. 3, 147–161 (1993)CrossRefGoogle Scholar
  112. 112.
    P. Taylor, M. Jones, Splitting criteria for regression trees. J. Stat. Comput. Simul. 55(4), 267–285 (1996)CrossRefzbMATHMathSciNetGoogle Scholar
  113. 113.
    L. Torgo, Functional models for regression tree leaves, in 14th International Conference on Machine Learning. ICML’97. (Morgan Kaufmann Publishers Inc., San Francisco, 1997), pp. 385–393Google Scholar
  114. 114.
    L. Torgo, A comparative study of reliable error estimators for pruning regression trees, in Iberoamerican Conference on Artificial Intelligence (Springer, Berlin, 1998), pp. 1–12Google Scholar
  115. 115.
    L. Torgo, Error estimators for pruning regression trees, in 10th European Conference on Machine Learning (Springer, Berlin, 1998), pp. 125–130Google Scholar
  116. 116.
    K.P. Unnikrishnan, K.P. Venugopal, Alopex: A correlation-based learning algorithm for feedforward and recurrent neural networks. Neural Comput. 6, 469–490 (1994)CrossRefGoogle Scholar
  117. 117.
    P.E. Utgoff, Perceptron trees: a case study in hybrid concept representations. Connect. Sci. 1(4), 377–391 (1989)CrossRefGoogle Scholar
  118. 118.
    P.E. Utgoff, N.C. Berkman, J.A. Clouse, Decision tree induction based on efficient tree restructuring. Mach. Learn. 29(1), 5–44 (1997)CrossRefzbMATHGoogle Scholar
  119. 119.
    P.E. Utgoff, C.E. Brodley, Linear machine decision trees. Technical Report. University of Massachusetts, Dept of Comp Sci (1991)Google Scholar
  120. 120.
    P.E. Utgoff, J.A. Clouse. A Kolmogorov-Smirnoff Metric for Decision Tree Induction. Technical Report. University of Massachusetts, pp. 96–3 (1996)Google Scholar
  121. 121.
    P. Utgoff, C. Brodley, An incremental method for finding multivariate splits for decision trees, in 7th International Conference on Machine Learning. pp. 58–65 (1990)Google Scholar
  122. 122.
    P.K. Varshney, C.R.P. Hartmann, J.M.J. de Faria, Application of information theory to sequential fault diagnosis. IEEE Trans. Comput. 31(2), 164–170 (1982)CrossRefzbMATHGoogle Scholar
  123. 123.
    D. Wang, L. Jiang, An improved attribute selection measure for decision tree induction, in: 4th International Conference on Fuzzy Systems and Knowledge Discovery. pp. 654–658 (2007)Google Scholar
  124. 124.
    Y. Wang, I.H. Witten, Induction of model trees for predicting continuous classes, in Poster papers of the 9th European Conference on Machine Learning (Springer, Berlin, 1997)Google Scholar
  125. 125.
    A.P. White, W.Z. Liu, Technical note: Bias in information-based measures in decision tree induction. Mach. Learn. 15(3), 321–329 (1994)zbMATHGoogle Scholar
  126. 126.
    S.S. Wilks, Mathematical Statistics (Wiley, New York, 1962)zbMATHGoogle Scholar
  127. 127.
    I.H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (Morgan Kaufmann, San Francisco, 1999). ISBN: 1558605525Google Scholar
  128. 128.
    C.T. Yildiz, E. Alpaydin, Omnivariate decision trees. IEEE Trans. Neural Netw. 12(6), 1539–1546 (2001)CrossRefGoogle Scholar
  129. 129.
    H. Zantema, H. Bodlaender, Finding small equivalent decision trees is hard. Int. J. Found. Comput. Sci. 11(2), 343–354 (2000)CrossRefMathSciNetGoogle Scholar
  130. 130.
    X. Zhou, T. Dillon, A statistical-heuristic feature selection criterion for decision tree induction. IEEE Trans. Pattern Anal. Mac. Intell. 13(8), 834–841 (1991)CrossRefGoogle Scholar

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© The Author(s) 2015

Authors and Affiliations

  • Rodrigo C. Barros
    • 1
    Email author
  • André C. P. L. F. de Carvalho
    • 2
  • Alex A. Freitas
    • 3
  1. 1.Faculdade de InformáticaPontifícia Universidade Católica do Rio Grande do SulPorto AlegreBrazil
  2. 2.Instituto de Ciências Matemáticas e de ComputaçãoUniversidade de São PauloSão CarlosBrazil
  3. 3.School of ComputingUniversity of KentCanterburyUK

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