On selection of representative object set for attribute reduction in set-valued information systems

T. Phung
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Abstract

In recent years, there have been many researches on tolerance rough set to solve attribute reduction and rule extraction problems in set-valued information systems. For attribute reduction problems, the most important issue is to minimize the time complexity of attribute reduction algorithms. There have been many methods for attribute reduction, but finding reducts in these methods is almost performed on initial object set. In this paper, we propose a method for selection representative object set from initial object set to solve attribute reduction problem in set-valued information systems. Because of the size of representative object set is smaller the size of initial object set, our method reduces significantly the time complexity of attribute reduction algorithms.
集值信息系统属性约简中代表性对象集的选择
近年来,针对集值信息系统中的属性约简和规则提取问题,对容差粗糙集进行了大量的研究。对于属性约简问题,最重要的问题是最小化属性约简算法的时间复杂度。属性约简的方法有很多,但这些方法的约简几乎都是在初始对象集上进行的。针对集值信息系统中的属性约简问题,提出了一种从初始对象集中选择具有代表性的对象集的方法。由于代表性对象集的大小比初始对象集的大小要小,该方法显著降低了属性约简算法的时间复杂度。
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