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Attribute reduction with fuzzy rough self-information measures
Information Sciences ( IF 8.1 ) Pub Date : 2020-11-28 , DOI: 10.1016/j.ins.2020.11.021
Changzhong Wang , Yang Huang , Weiping Ding , Zehong Cao

The fuzzy rough set is one of the most effective methods for dealing with the fuzziness and uncertainty of data. However, in most cases this model only considers the information provided by the lower approximation of a decision when it is used to attribute reduction. In a realistic environment, the uncertainty of information is related to lower approximation as well as upper approximation. In this study, we construct four kinds of uncertainty measures by combining fuzzy rough approximations with the concept of self-information. These uncertainty measures can be employed to evaluate the classification ability of attribute subsets. The relationships between these measures are discussed in detail. It is proven that the fourth measure, called relative decision self-information, is better for attribute reduction than the other measures because it considers both the lower and upper approximations of a fuzzy decision. The proposed measures are generalizations of classical measures based on fuzzy rough sets. Finally, we have designed a greedy algorithm for attribute reduction. We validate the effectiveness of the proposed method by comparing the experimental results for efficiency and accuracy with those of three other algorithms using fundamental data.



中文翻译:

模糊粗糙自信息测度的属性约简

模糊粗糙集是处理数据模糊性和不确定性的最有效方法之一。但是,在大多数情况下,该模型仅将决策的较低近似值提供给归因于归约的信息。在现实环境中,信息的不确定性与上下近似和上下近似有关。在这项研究中,我们将模糊粗略近似与自我信息概念相结合,构造出四种不确定性度量。这些不确定性度量可用于评估属性子集的分类能力。这些措施之间的关系进行了详细讨论。事实证明,第四种方法称为相对决策自我信息,与属性度量相比,它比其他度量更好,因为它考虑了模糊决策的上下近似。提出的措施是基于模糊粗糙集的经典措施的推广。最后,我们设计了一种用于属性约简的贪婪算法。通过将实验结果的效率和准确性与使用基础数据的其他三种算法的实验结果进行比较,我们验证了该方法的有效性。

更新日期:2020-12-11
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