| 基于粗糙集的归纳推理检索方法 |
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| 引用本文： | 张光前,邓贵仕,吕文颜.基于粗糙集的归纳推理检索方法[J].计算机工程,2003,29(16):23-24,105. |
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| 作者姓名： | 张光前 邓贵仕 吕文颜 |
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| 作者单位： | 大连理工大学系统工程研究所,大连,116024 |
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| 基金项目： | 博士生专项基金资助项目(1999014104) |
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| 摘 要： | 归纳推理检索是基于事例推理（CBR）中常用的检索方法之一，是基于ID3算法的检索方法。文章在基干事例推理方法的背景下论证了事例的属性的重要性和冗余之间的关系，并在此基础上从属性相对于其属性的重要性角度来构造启发函数。和ID3算法相比较，该算法不但降低了计算复杂性，而且在一定程度上可以消除样本中的噪声，使归纳推理检索方法的检索效率有所提高。
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| 关 键 词： | 基于事例推理 归纳推理检索 决策树 粗集 重要度 |
| 文章编号： | 1000-3428(2003)16-0023-02 |
Inductive Retrieval Strategy Based on Rough Set |
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| ZHANG Guangqian,DENG Guishi,LV Wenyan.Inductive Retrieval Strategy Based on Rough Set[J].Computer Engineering,2003,29(16):23-24,105. |
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| Authors: | ZHANG Guangqian DENG Guishi LV Wenyan |
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| Abstract: | Inductive retrieval is one of the retrieval methods commonly used in case-based reasoning systems, which is based on ID3. In this article, the relationship between weightiness and redundancy of attributes is demonstrated. A decision tree algorithm is given based on rough set in the point of view of the instances attributes weightiness. It has advantages in computational complexity and in gaining more effective cases retrieval. Key words Case-based reasoning (CBR);Inductive retrieval;Decision tree;Rough set;Weightiness |
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| Keywords: | Case-based reasoning (CBR) Inductive retrieval Decision tree Rough set Weightiness |
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