| 基于相似粗糙集的范例推理技术 |
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| 引用本文： | 卢亦娟,倪志伟,胡彩平. 基于相似粗糙集的范例推理技术[J]. 计算机工程, 2003, 29(19): 50-51,116 |
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| 作者姓名： | 卢亦娟 倪志伟 胡彩平 |
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| 作者单位： | 计算智能与信号处理国家教育部重点实验室,安徽大学计算机系,合肥,230039 |
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| 基金项目： | 国家自然科学基金资助项目(60273043)，安徽省教育厅自然科学基金资助项目(2002kj004) |
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| 摘 要： | 讨论了经典粗糙集理论在特征项约简过程中，造成的范例相似度测量误差问题，并提出用相似粗糙集约简特征项和获取特征项权重的过程与算法，力求提高约简的精确性和保持约简后范例库的良好性能。
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| 关 键 词： | 相似粗糙集 范例推理 特征项约简 差别矩阵 特征项权值 |
| 文章编号： | 1000-3428(2003)19-0050-02 |
Technique Used in CBR Based on Similarity Rough Set |
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| LU Yijuan,NI Zhiwei,HU Caiping. Technique Used in CBR Based on Similarity Rough Set[J]. Computer Engineering, 2003, 29(19): 50-51,116 |
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| Authors: | LU Yijuan NI Zhiwei HU Caiping |
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| Abstract: | This paper discusses a deviation problem of measuring case similarity, which happens in the process of features reduction with classical rough set theory. In order to improve reduction accuracy and keep good quality of reduced case base, this paper proposes an algorithm based on similarity rough set theory to reduce features and compute features weight. |
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| Keywords: | Similarity rough set Case-based reasoning(CBR) Features reduction indiscernibility matrix Features weight |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
