On rule acquisition methods for data classification in heterogeneous incomplete decision systems

doi:10.1016/j.knosys.2020.105472

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	On rule acquisition methods for data classification in heterogeneous incomplete decision systems
	Meng, Zuqiang 1; Shi, Zhongzhi 2
刊名	KNOWLEDGE-BASED SYSTEMS
	2020-04-06
卷号	193 页码:22
关键词	Rough set Heterogeneous incomplete decision systems Rule acquisition Data classification Reduction
ISSN号	0950-7051
DOI	10.1016/j.knosys.2020.105472
英文摘要	In the age of big data, lots of data obtained is low-quality data characterized by heterogeneousness and incompleteness, referred to as heterogeneous incomplete decision systems (HIDSs) in this paper. Data classification is an important task in machine learning, with the ability to discover valuable knowledge hidden in HIDSs. However, systematic studies on data classification in HIDSs are rarely reported. Especially, there is a lack of adaptive classification methods for HIDSs, which can deal directly with heterogeneous incomplete data and do not require prior discretization of numerical attributes or filling in missing values. In this paper, a unified representation model, called parameterized tolerance granulation model (PTGM), is proposed to deal with heterogeneous incomplete data. And the principle of an adaptive granulation method of constructing appropriate PTGMs is also described using difference-based collaborative optimization. Based on PTGMs, decision logic language is used to describe classifiers consisting of decision rules satisfying given conditions. Then, a discernibility function-based and a heuristic function-based classification methods are proposed to obtain all optimized rule sets (classifiers) and to generate a particular optimized rule set, respectively. The heuristic function-based method is actually an adaptive classification method, which can deal directly with heterogeneous incomplete data. Furthermore, detailed theoretical analyses are given to illustrate the correctness and effectiveness of the proposed methods. The experimental results show that the proposed methods are effective and have obvious advantages in directly handling heterogeneous incomplete data. (c) 2020 Elsevier B.V. All rights reserved.
资助项目	National Natural Science Foundation of China[61762009]
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000523558800009
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/14064]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Meng, Zuqiang
作者单位	1.Guangxi Univ, Coll Comp Elect & Informat, Nanning 530004, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Meng, Zuqiang,Shi, Zhongzhi. On rule acquisition methods for data classification in heterogeneous incomplete decision systems[J]. KNOWLEDGE-BASED SYSTEMS,2020,193:22.
APA	Meng, Zuqiang,&Shi, Zhongzhi.(2020).On rule acquisition methods for data classification in heterogeneous incomplete decision systems.KNOWLEDGE-BASED SYSTEMS,193,22.
MLA	Meng, Zuqiang,et al."On rule acquisition methods for data classification in heterogeneous incomplete decision systems".KNOWLEDGE-BASED SYSTEMS 193(2020):22.