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Um algoritmo de indução de árvore de decisão baseado em agrupamento

Rodrigo C. BarrosMárcio P. BasgaluppAndré C.P.L.F. de CarvalhoMarcos G. Quiles

Decision tree induction algorithms are well known techniques for assigning objects to predefined categories in a transparent fashion. Most decision tree induction algorithms rely on a greedy top-down recursive strategy for growing the tree, and pruning techniques to avoid overfitting. Even though such a strategy has been quite successful in many problems, it falls short in several others. For instance, there are cases in which the hyper-rectangular surfaces generated by these algorithms can only map the problem description after several sub-sequential partitions, which results in a large and incomprehensible tree. Hence, we propose a new decision tree induction algorithm based on clustering which seeks to provide more accurate models and/or shorter descriptions, which are comprehensible for the end-user. We do not base our performance analysis solely on the straightforward comparison of our proposed algorithm to baseline methods. Instead, we propose a data-dependent analysis in order to look for evidences which may explain in which situations our algorithm outperforms a well-known decision tree induction algorithm.

http://www.lbd.dcc.ufmg.br/colecoes/enia/2011/0013.pdf

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