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- W10403224 abstract "The induction of decision trees from data is a well-known method for learning classifiers. The success of this method depends to a high degree on the measure used to select the next attribute, which, if tested, will improve the accuracy of the classification. This paper examines some possibility-based selection measures and compares them to probabilityand information-based measures on real world datasets. The results show that possibility-based measures do not much worse with regard to classification accuracy, in certain cases they seem to do even slightly better." @default.
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- W10403224 date "2004-01-01" @default.
- W10403224 modified "2023-09-26" @default.
- W10403224 title "Concepts for Probabilistic and Possibilistic Induction of Decision Trees on Real World Data" @default.
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