Knowledge reduction of dynamic covering decision information systems when varying covering cardinalities
Knowledge reduction of dynamic covering decision information systems when varying covering cardinalities
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DOI：
10.1016/j.ins.2016.01.099
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发表时间：
2016-06
期刊：
影响因子：
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通讯作者：
Guangming Lang;Duoqian Miao;Tian Yang;Mingjie Cai
中科院分区：
文献类型：
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作者：
Guangming Lang;Duoqian Miao;Tian Yang;Mingjie Cai
在基于覆盖的粗糙集理论中，当覆盖的基数因对象的迁移和迁移而发生变化时，采用非增量方法进行动态覆盖决策信息系统的知识约简是耗时的。由于计算集合的近似是动态覆盖决策信息系统知识约简的重要步骤，因此分别使用第1类和第2类特征矩阵计算集合的第二和第六下近似和上近似的有效方法是必不可少的。在本文中，我们提供了增量的方法来计算的类型-1和类型-2的特征矩阵的动态覆盖，其基数随着移民和移民的对象。我们还设计了增量算法来计算第二和第六下和上集近似。实验结果表明，增量式方法有效地提高了集合近似计算的效率。最后，我们用几个例子来说明的增量方法的可行性，知识约简的动态覆盖决策信息系统时，增加覆盖的基数。
In covering-based rough set theory, non-incremental approaches are time-consuming for performing knowledge reduction of dynamic covering decision information systems when the cardinalities of coverings change as a result of object immigration and emigration. Because computing approximations of sets is an important step for knowledge reduction of dynamic covering decision information systems, efficient approaches to calculating the second and sixth lower and upper approximations of sets using the type-1 and type-2 characteristic matrices, respectively, are essential. In this paper, we provide incremental approaches to computing the type-1 and type-2 characteristic matrices of dynamic coverings whose cardinalities vary with the immigration and emigration of objects. We also design incremental algorithms to compute the second and sixth lower and upper set approximations. Experimental results demonstrate that the incremental approaches effectively improve the efficiency of set approximation computation. Finally, we employ several examples to illustrate the feasibility of the incremental approaches for knowledge reduction of dynamic covering decision information systems when increasing the cardinalities of coverings.