| 一种基于信息粒度的动态属性约简求解算法 |
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| 引用本文： | 王永生,郑雪峰,锁延锋.一种基于信息粒度的动态属性约简求解算法[J].计算机科学,2015,42(4):213-216. |
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| 作者姓名： | 王永生 郑雪峰 锁延锋 |
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| 作者单位： | 1. 北京科技大学计算机与通信工程学院 北京100083
2. 北京科技大学材料领域知识工程北京市重点实验室 北京100083 |
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| 基金项目： | 本文受国家自然科学基金项目(61163025),材料领域知识工程北京市重点实验室2012年度阶梯计划项目(Z121101002812005)资助 |
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| 摘 要： | 动态属性约简是粗糙集理论的重要研究内容之一.针对动态决策表构造了一种基于信息粒度的动态属性约简模型,详细分析了决策表中出现新属性动态增加时信息粒度的增量式计算方法;在此基础上,以信息粒度作为启发信息,设计了一种动态属性约简求解算法,该算法能有效利用原决策表的属性约简结果和信息粒度来降低算法的计算复杂度,并使得约简结果具有较好传承性;最后通过算例分析和实验比较进一步验证了本算法的可行性和有效性.
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| 关 键 词： | 信息粒度 动态属性约简 动态决策表 正区域 粗糙集理论 |
Dynamic Algorithm for Computing Attribute Reduction Based on Information Granularity |
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| WANG Yong-sheng,ZHENG Xue-feng and SUO Yan-feng.Dynamic Algorithm for Computing Attribute Reduction Based on Information Granularity[J].Computer Science,2015,42(4):213-216. |
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| Authors: | WANG Yong-sheng ZHENG Xue-feng and SUO Yan-feng |
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| Affiliation: | School of Computer and Communication Engineering,University of Science and Technology Beijing,Beijing 100083,China Beijing Key Laboratory of Knowledge Engineering for Materials Science,University of Science and Technology Beijing,Beijing 100083,China,School of Computer and Communication Engineering,University of Science and Technology Beijing,Beijing 100083,China Beijing Key Laboratory of Knowledge Engineering for Materials Science,University of Science and Technology Beijing,Beijing 100083,China and School of Computer and Communication Engineering,University of Science and Technology Beijing,Beijing 100083,China Beijing Key Laboratory of Knowledge Engineering for Materials Science,University of Science and Technology Beijing,Beijing 100083,China |
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| Abstract: | Dynamic attribute reduction is one of the important issues in rough set theory.A dynamic attribute reduction model based on information granularity was constructed in dynamic decision table,and an incremental approach for computing information granularity was discussed in detail when some new attribute set is added into decision table.On this basis,a dynamic attribute reduction algorithm was proposed by using information granularity as the heuristic information.The proposed algorithm can use attribute reduction and information granularity of original decision table,which can effectively reduce the computational complexity,so that the attribute reduction has better inheritance.Finally,the example and experimental comparison indicate the feasibility and validity of the proposed algorithm. |
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| Keywords: | Information granularity Dynamic attribute reduction Dynamic decision table Positive region Rough set theory |
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