| 基于知识粗糙度的决策树生成方法 |
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| 引用本文： | 路红梅. 基于知识粗糙度的决策树生成方法[J]. 数字社区&智能家居, 2007, 0(9): 1412-1412,1442 |
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| 作者姓名： | 路红梅 |
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| 作者单位： | 宿州学院人工智能与数据挖掘研究室,安徽宿州234000 |
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| 基金项目： | 安徽省教育厅自然科学基金项目（2006kj091B） |
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| 摘 要： | 决策树是一种重要的数据分类方法,测试属性的选择直接影响到决策树中结点的个数和深度,本文提出了一种基于知识粗糙度的方法。通过比较我们发现：在决策树的构造上,粗集理论中知识粗糙度的方法计算量较小,构造的决策树比经典ID3算法简洁,并且具有较高的分类精度。
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| 关 键 词： | 决策树 粗糙集 知识粗糙度 |
| 文章编号： | 1009-3044（2007）17-31412-01 |
| 修稿时间： | 2007-08-03 |
A Knowledge Roughness Based Approach to Decision Tree Construction |
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| LU Hong-mei. A Knowledge Roughness Based Approach to Decision Tree Construction[J]. Digital Community & Smart Home, 2007, 0(9): 1412-1412,1442 |
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| Authors: | LU Hong-mei |
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| Affiliation: | Department of Artificial intelligence and Datamining, Suzhou College, Suzhou 234000,China |
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| Abstract: | Decision Tree is one of the most important method for datamining,the choice of testing attribute will touch the depth and nodes of decision tree, In this paper we bring forward a new approach to decision tree construction based on knowledge roughness , By comparation we find that the new method has less compute capacity and constructed decision tree is concision with higher classify precision. |
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| Keywords: | decision tree rough set knowledge roughness |
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