On the handling of continuous-valued attributes in decision tree generation
作者:Usama M. Fayyad, Keki B. Irani
摘要
We present a result applicable to classification learning algorithms that generate decision trees or rules using the information entropy minimization heuristic for discretizing continuous-valued attributes. The result serves to give a better understanding of the entropy measure, to point out that the behavior of the information entropy heuristic possesses desirable properties that justify its usage in a formal sense, and to improve the efficiency of evaluating continuous-valued attributes for cut value selection. Along with the formal proof, we present empirical results that demonstrate the theoretically expected reduction in evaluation effort for training data sets from real-world domains.
论文关键词:Induction, empirical concept learning, decision trees, information entropy minimization, discretization, classification
论文评审过程:
论文官网地址:https://doi.org/10.1007/BF00994007