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ON THE INDUCTION OF DECISION TREES FOR MULTIPLE CONCEPT LEARNING (MACHINE LEARNING).

USAMA M. FAYYAD

出版	University of MICHIGAN, 1991
URL	http://books.google.com.hk/books?id=jVgeAQAAMAAJ&hl=&source=gbs_api

註釋

heuristic. The results serve to give a better understanding of the entropy measure, to point out desirable properties that justify its usage in a formal sense, and to improve the efficiency of evaluating continuous-valued attributes for cut point selection. We then proceed to extend the binary discretization algorithm to derive multiple interval quantizations. We justify our criterion for deciding the intervals using decision-theoretic principles. Empirical results demonstrate improved efficiency and