A fuzzy similarity-based rough set approach for attribute selection in set-valued information systems

被引:0
|
作者
Shivani Singh
Shivam Shreevastava
Tanmoy Som
Gaurav Somani
机构
[1] BHU,DST
[2] IIT (BHU),Centre for Interdisciplinary Mathematical Sciences, Institute of Science
来源
Soft Computing | 2020年 / 24卷
关键词
Set-valued data; Rough set; Fuzzy tolerance relation; Degree of dependency; Attribute selection;
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暂无
中图分类号
学科分类号
摘要
Databases obtained from different search engines, market data, patients’ symptoms and behaviours, etc., are some common examples of set-valued data, in which a set of values are correlated with a single entity. In real-world data deluge, various irrelevant attributes lower the ability of experts both in speed and in predictive accuracy due to high dimension and insignificant information, respectively. Attribute selection is the concept of selecting those attributes that ideally are necessary as well as sufficient to better describe the target knowledge. Rough set-based approaches can handle uncertainty available in the real-valued information systems after the discretization process. In this paper, we introduce a novel approach for attribute selection in set-valued information system based on tolerance rough set theory. The fuzzy tolerance relation between two objects using a similarity threshold is defined. We find reducts based on the degree of dependency method for selecting best subsets of attributes in order to obtain higher knowledge from the information system. Analogous results of rough set theory are established in case of the proposed method for validation. Moreover, we present a greedy algorithm along with some illustrative examples to clearly demonstrate our approach without checking for each pair of attributes in set-valued decision systems. Examples for calculating reduct of an incomplete information system are also given by using the proposed approach. Comparisons are performed between the proposed approach and fuzzy rough-assisted attribute selection on a real benchmark dataset as well as with three existing approaches for attribute selection on six real benchmark datasets to show the supremacy of proposed work.
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页码:4675 / 4691
页数:16
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