文献详情
Discernibility matrix based incremental feature selection on fused decision tables
文献类型：期刊
通讯作者：Zhao, SY (reprint author), Renmin Univ China, Key Lab Data Engn & Knowledge Engn, MOE, Beijing, Peoples R China.
期刊名称：INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
年：2020
卷：118
页码：1-26
ISSN：0888-613X
关键词：Incremental learning; Feature selection; Fuzzy rough sets; Discernibility matrix; Fused decision table
所属部门：信息学院；数据工程与知识工程教育部重点实验室
摘要：In rough set philosophy, each set of data can be seen as a fuzzy decision table. Since a decision table dynamically increases with time and space, these decision tables are integrated into a new one called fused decision table. In this paper, we focus on designing an incremental feature selection method on fused decision table by optimizing the space constraint of storing discernibility matrix. Here discernibility matrix is a known way of discernibility information measure in rough set theory. T ...More
In rough set philosophy, each set of data can be seen as a fuzzy decision table. Since a decision table dynamically increases with time and space, these decision tables are integrated into a new one called fused decision table. In this paper, we focus on designing an incremental feature selection method on fused decision table by optimizing the space constraint of storing discernibility matrix. Here discernibility matrix is a known way of discernibility information measure in rough set theory. This paper applies the quasi/pseudo value of discernibility matrix rather than the true value of discernibility matrix to design an incremental mechanism. Unlike those discernibility matrix based non-incremental algorithms, the improved algorithm needs not save the whole discernibility matrix in main memory, which is desirable for the large data sets. More importantly, with the increment of decision tables, the discernibility matrix-based feature selection algorithm could constrain the computational cost by applying efficient information updating techniques-quasi/pseudo approximation operators. Finally, our experiments reveal that the proposed algorithm needs less computational cost, especially less occupied space, on the condition that the accuracy is limitedly lost. (C) 2019 Elsevier Inc. All rights reserved. ...Hide

DOI：10.1016/j.ijar.2019.11.010
百度学术：Discernibility matrix based incremental feature selection on fused decision tables
语言：外文
人气指数：1
浏览次数：1
基金：National Key Research Develop Plan [2016YFB1000702]; National Key R&D Program of China [2017YFB1400700]; NSFCNational Natural Science Foundation of China [61732006, 61772536, 61532021, 61772537, 61702522]; National Basic Research Program of China (973)National Basic Research Program of China [2014CB340402]; NSSFC [12ZD220]
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