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

doi:10.1016/j.knosys.2020.105472

	On rule acquisition methods for data classification in heterogeneous incomplete decision systems
	Meng, Zuqiang 1; Shi, Zhongzhi 2
	2020-04-06
发表期刊	KNOWLEDGE-BASED SYSTEMS
ISSN	0950-7051
卷号	193 页码:22
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.
关键词	Rough set Heterogeneous incomplete decision systems Rule acquisition Data classification Reduction
DOI	10.1016/j.knosys.2020.105472
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61762009]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000523558800009
出版者	ELSEVIER
引用统计	被引频次：11[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/14065
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.