| 一种基于属性重要性的属性约简启发式算法 |
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| 引用本文： | 叶明全,胡学钢.一种基于属性重要性的属性约简启发式算法[J].河南师范大学学报(自然科学版),2008,36(5). |
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| 作者姓名： | 叶明全 胡学钢 |
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| 作者单位： | 1. 皖南医学院,计算机教研室,安徽,芜湖,241002
2. 合肥工业大学,计算机与信息学院,合肥,230009 |
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| 基金项目： | 安徽省高校省级自然科学基金 |
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| 摘 要： | 属性约简是粗糙集理论研究中的关键问题之一.文中定义了一种新的属性重要性度量准则,克服了多值偏向性问题,并给出一种新的属性约简算法.该算法以核属性集为初始约简集合,以新的属性重要性度量准则为启发信息,通过逐步加入相对于决策而言重要的条件属性来求取最小约简.实例分析表明该算法是有效的.
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| 关 键 词： | 粗糙集 属性约简 属性重要性 多值偏向 |
Heuristic Algorithm for Reduction Based on Attribute Significance |
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| YE Ming-quan,HU Xue-gang.Heuristic Algorithm for Reduction Based on Attribute Significance[J].Journal of Henan Normal University(Natural Science),2008,36(5). |
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| Authors: | YE Ming-quan HU Xue-gang |
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| Abstract: | Attribute reduction is one of the key problems in the research on rough set theory.In order to avoid variety bias,a new messure of attribute significance is defined.And based on this method,a new algorithm of attribute reduction is proposed.With the core attributes as the initial reduction,this algorithm uses the attribute significance as heuristic information,and finds the minimal reduction.The results from an example show that this algorithm is effective. |
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| Keywords: | rough sets attribute reduction attribute significance variety bias |
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