On the handling of fuzziness for continuous-valued attributes in decision tree generation
作者： Wang Xizhao , Jiarong Hong
DOI: 10.1016/S0165-0114(97)00030-4
关键词: Membership function 、 Decision tree learning 、 Linear partial information 、 Incremental decision tree 、 ID3 algorithm 、 Artificial intelligence 、 Decision tree 、 Tree (data structure) 、 Fuzzy number 、 Mathematics
摘要: Abstract In this paper, fuzziness existing in the process of generating decision trees by discretizing continuous-valued attributes is considered. a sense better way to express via fuzzy numbers presented using possibility theory. The fact that selection membership functions class symmetric distributions does not influence tree generation proved. validity classify future examples explained. On basis likelihood maximization, algorithm revised. revised leads more reasonable and natural trees.
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