Automatic Design of Decision-Tree Induction Algorithms by Rodrigo C. Barros, André C.P.L.F de Carvalho, Alex A.

By Rodrigo C. Barros, André C.P.L.F de Carvalho, Alex A. Freitas

Presents a close examine of the most important layout elements that represent a top-down decision-tree induction set of rules, together with points akin to break up standards, preventing standards, pruning and the ways for facing lacking values. while the method nonetheless hired these days is to exploit a 'generic' decision-tree induction set of rules whatever the info, the authors argue at the advantages bias-fitting method may perhaps deliver to decision-tree induction, during which the final word aim is the automated iteration of a decision-tree induction set of rules adapted to the appliance area of curiosity. For such, they talk about how you can successfully observe the main appropriate set of elements of decision-tree induction algorithms to house a wide selection of purposes 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 valuable for computer studying and evolutionary computation scholars and researchers alike.

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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. A. -S. Lee, Data mining criteria for tree-based regression and classification, in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

By recursively repeating this strategy, we end up with a decision tree in which the more obvious discriminations are done first, and the more subtle distinctions are postponed to lower levels. Landeweerd et al. 43) where μyi is the mean attribute vector of class yi and is the covariance matrix pooled over all classes. 5 We believe these issues are among the main reasons why bottom-up induction has not become as popular as topdown induction. For alleviating these problems, Barros et al. [4] propose a bottom-up induction algorithm named BUTIA that combines EM clustering with SVM classifiers.

B. , 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) 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) 54. A. Karalic, ˇ Employing linear regression in regression tree leaves, 10th European Conference on Artificial Intelligence. ECAI’92 (Wiley, New York, 1992) 55. V. Kass, An exploratory technique for investigating large quantities of categorical data.

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