期刊
MACHINE LEARNING
卷 55, 期 1, 页码 53-69出版社
KLUWER ACADEMIC PUBL
DOI: 10.1023/B:MACH.0000019804.29836.05
关键词
data mining; machine learning; discretization; data analysis
In supervised machine learning, some algorithms are restricted to discrete data and have to discretize continuous attributes. Many discretization methods, based on statistical criteria, information content, or other specialized criteria, have been studied in the past. In this paper, we propose the discretization method Khiops,(1) based on the chi-square statistic. In contrast with related methods ChiMerge and ChiSplit, this method optimizes the chi-square criterion in a global manner on the whole discretization domain and does not require any stopping criterion. A theoretical study followed by experiments demonstrates the robustness and the good predictive performance of the method.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据