By Rodrigo C. Barros, André C.P.L.F de Carvalho, Alex A. Freitas
Presents a close research of the foremost layout elements that represent a top-down decision-tree induction set of rules, together with facets akin to break up standards, preventing standards, pruning and the ways for facing lacking values. while the tactic nonetheless hired these days is to take advantage of a 'generic' decision-tree induction set of rules whatever the facts, the authors argue at the merits bias-fitting approach may carry to decision-tree induction, during which the last word objective is the automated new release of a decision-tree induction set of rules adapted to the appliance 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 functions in the course of the paradigm of evolutionary computation, following the emergence of a unique box known as hyper-heuristics.
"Automatic layout of Decision-Tree Induction Algorithms" will be hugely worthwhile for computing device studying and evolutionary computation scholars and researchers alike.
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Additional info for Automatic Design of Decision-Tree Induction Algorithms
Learn. Res. 8, 891–933 (2007) 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) 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) 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.
Comput. 100(4), 404–408 (1977) 39. 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.
6 Nominal attributes are not used more than once in a given subtree. 40 2 Decision-Tree Induction Even though a lot of effort has been employed in the design of a non-greedy decision-tree induction algorithm, it is still debatable whether the proposed attempts can consistently obtain better results than the greedy top-down framework. Most of the times, the gain in performance obtained with a non-greedy approach is not sufficient to compensate for the extra computational effort. 5 Chapter Remarks In this chapter, we presented the main design choices one has to face when programming a decision-tree induction algorithm.