| 基于粗糙集的突发事件属性约简方法 |
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| 引用本文： | 仲秋雁,王然,曲毅.基于粗糙集的突发事件属性约简方法[J].运筹与管理,2018,27(1):89-95. |
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| 作者姓名： | 仲秋雁 王然 曲毅 |
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| 作者单位： | 大连理工大学 管理科学与工程学院,辽宁 大连 116024 |
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| 摘 要： | 针对突发事件不完备信息系统中的原始数据存在大量属性冗余的问题,提出一种基于粗糙集的不完备信息系统属性约简方法,以剔除冗余属性,提高知识清晰度。首先对缺失、冗余、噪声以及连续型数据进行预处理;然后进行属性分类,将属性分为条件属性与决策属性,进而建立决策表;最后根据决策表的特征,结合有序加权平均算子的思想,提出一种基于属性重要度的启发式属性约简算法。文末,通过实例验证了方法的正确性与有效性,并利用该方法实现了火灾数据的属性约简。
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| 关 键 词： | 突发事件 属性约简 粗糙集 不完备信息系统 属性重要度 |
| 收稿时间： | 2014-08-13 |
A Method of Attribute Reduction for Emergency Based on Rough Set |
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| ZHONG Qiu-yan,WANG Ran,QU Yi.A Method of Attribute Reduction for Emergency Based on Rough Set[J].Operations Research and Management Science,2018,27(1):89-95. |
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| Authors: | ZHONG Qiu-yan WANG Ran QU Yi |
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| Affiliation: | School of Management Science and Engineering, Dalian University of Technology, Dalian 116024, China |
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| Abstract: | With the improvement of our emphasis on emergencies and degree of informationization, large amounts of raw data in emergencies are accumulated, which contain much valuable knowledge and rules to be excavated. As emergencies involve a lot of objects in affected area, the number of attributes in raw data related to the emergency is considerable. Since there are many redundant attributes, the relationships among the factors are obscure. In addition, the missing values we cannot get lead to the incompleteness of emergency information system. For the problem of serious redundancy of attributes in raw data of emergency incomplete information systems, this paper proposes a method of attribute reduction for incomplete information systems of emergency based on rough set to eliminate redundant attributes and improve the definition of knowledge. Firstly, raw data are prepossessed. Missing data are reserved by using “*” instead. Continuous data are discretized considering the specific feature of the attribute in emergency. Redundant data and noisy data are identified by building a similarity matrix of all objects in the information system and then deleted. Then, the attributes are classified to two categories, one is conditional attribute, the other is decision attribute. On this base, we establish a decision table. Next, a method to measure the significance of attributes, which reflects the contribution of an attribute to identify the objects, is proposed combining with the ideas of the ordered weighted averaging operator. By using significance of attributes as heuristic information, we propose a heuristic attribute reduction algorithm. At the end of this paper, two examples are conducted. The first one is a classical decision table. The result shows that the algorithm in this research is correct and effective. The second one is an information table of fire events which is processed by the method in this paper. Through the second example, we show the application of the method and analyze the reduction of the information table. |
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| Keywords: | emergency attribute reduction rough set incomplete information system attribute significance |
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