By Rodrigo C. Barros, André C.P.L.F de Carvalho, Alex A. Freitas
Presents a close learn of the main layout elements that represent a top-down decision-tree induction set of rules, together with points similar to cut up standards, preventing standards, pruning and the methods for facing lacking values. while the method nonetheless hired these days is to exploit a 'generic' decision-tree induction set of rules whatever the facts, the authors argue at the advantages bias-fitting technique may perhaps convey to decision-tree induction, during which the final word target is the automated iteration of a decision-tree induction set of rules adapted to the applying area of curiosity. For such, they speak about how you can successfully observe the main appropriate set of parts of decision-tree induction algorithms to accommodate a large choice of purposes throughout the paradigm of evolutionary computation, following the emergence of a singular box referred to as hyper-heuristics.
"Automatic layout of Decision-Tree Induction Algorithms" will be hugely helpful for desktop studying and evolutionary computation scholars and researchers alike.
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Methods Softw. 1, 23–34 (1992) 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) 12. L. , Classification and Regression Trees (Wadsworth, Belmont, 1984) 13. L. Breslow, D. Aha, Simplifying decision trees: a survey. Knowl. Eng. Rev. 12(01), 1–40 (1997) 14. E. E. Utgoff, Multivariate versus univariate decision trees. Technical Report. Department of Computer Science, University of Massachusetts at Amherst (1992) 15.
B. S. J. Delp, An iterative growing and pruning algorithm for classification tree design. IEEE Int. Conf. Syst. Man Cybern. 2, 818–823 (1989) 40. W. Gillo, MAID: A Honeywell 600 program for an automatised survey analysis. Behav. Sci. 17, 251–252 (1972) 41. M. Gleser, M. Collen, Towards automated medical decisions. Comput. Biomed. Res. 5(2), 180–189 (1972) 42. A. H. Kruskal, Measures of association for cross classifications. J. Am. Stat. Assoc. 49(268), 732–764 (1954) 43. T. , Lower bounds on learning decision lists and trees.
7(3), 209–216 (1997) 76. N. 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) 77. K. Murthy, S. S. Salzberg, A system for induction of oblique decision trees. J. Artif. Intell. Res. 2, 1–32 (1994) 78. K. Murthy, Automatic construction of decision trees from data: A multi-disciplinary survey. Data Min. Knowl. Discov. 2(4), 345–389 (1998) 79. K. Murthy, S. Salzberg, Lookahead and pathology in decision tree induction, in 14th International Joint Conference on Artificial Intelligence.
Automatic Design of Decision-Tree Induction Algorithms by Rodrigo C. Barros, André C.P.L.F de Carvalho, Alex A. Freitas