Feature Construction in Structural Decision Trees

机译：结构决策树中的特征构造

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STRUCT is a system that learns structural decision trees from positive and negative examples. The algorithm uses a modification of Pagallo and Haussler's FRINGE algorith-m to construct new features in a first-order representation. Experiments compare the d-ifferent feature construction strategies. The results show that a modified FRINGE algorithm improves accuracy, but that it is sensitive to the distribution of the examples.

机译：STRUCT是一个从正面和负面例子中学习结构决策树的系统。该算法使用Pagallo和Haussler的FRINGE算法-m的修改来构造一阶表示形式的新特征。实验比较了不同的特征构建策略。结果表明，改进的FRINGE算法提高了精度，但对示例的分布敏感。

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来源
《Machinee learning》|1991年|218-222|共5页
会议地点 Evanston IL(US);Evanston IL(US)
作者
Larry Watanabe; Larry Rendell;
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作者单位

Beckman Institute and Dept. of Computer Science University of Illinois Urbana, IL 61801;

Beckman Institute and Dept. of Computer Science University of Illinois Urbana, IL 61801;

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原文格式 PDF
正文语种 eng
中图分类计算机的应用;
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Feature Construction in Structural Decision Trees

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