An incremental attribute reduction approach based on knowledge granularity for incomplete decision systems
- PDF / 595,391 Bytes
- 15 Pages / 595.276 x 790.866 pts Page_size
- 37 Downloads / 225 Views
(0123456789().,-volV)(0123456789(). ,- volV)
ORIGINAL PAPER
An incremental attribute reduction approach based on knowledge granularity for incomplete decision systems Chucai Zhang1 • Jianhua Dai1 Received: 16 January 2019 / Accepted: 6 May 2019 Ó Springer Nature Switzerland AG 2019
Abstract Attribute reduction is a core issue in rough set theory. In recent years, with the fast development of data processing tools, information systems may increase quickly in objects over time. How to update attribute reducts efficiently becomes more and more important. Although some approaches have been proposed, they are used for complete decision systems. There are relatively few studies on incremental attribute reduction for incomplete decision systems. We introduce knowledge granularity, that can be obtained by the tolerance classes, to measure the uncertainty in incomplete decision systems. Furthermore, we propose incremental attribute reduction algorithms for incomplete decision systems when adding multiple objects and when deleting multiple objects, respectively. Finally, experimental results show that the proposed incremental approach is effective and efficient to update attribute reducts with the variation of objects in incomplete decision systems. Keywords Incremental attribute reduction Knowledge granularity Incomplete decision system Rough sets
1 Introduction Rough set theory, proposed by Pawlak (1982), is a powerful mathematical tool to deal with uncertainty, granularity, and incompleteness of knowledge in information systems. It has been applied successfully in many fields including machine learning, intelligent data analysis, decision making, knowledge engineering, disease diagnosis, and so on (Chen and Tanuwijaya 2011; Derrac et al. 2012; Chen and Chang 2011; Lin et al. 2011; Formica 2012; Wafo Soh et al. 2018; Min et al. 2011; Li et al. 2012, 2016; Wang et al. 2019a; Chen et al. 2013; Liu et al. 2018; D’Eer et al. 2016; Liao et al. 2018; Jothi and Hannah 2016; Zhan et al. 2017; Koley et al. 2016; Afridi et al. 2018; Xu et al. 2017). Since rough set theory can achieve a subset of all attributes which preserves the discernible ability of original features, using the data only with no additional information, it has been widely applied in attribute reduction (also called attribute selection or feature & Jianhua Dai [email protected] 1
Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha 410081, China
selection) (Dai et al. 2017a; Raza and Qamar 2016; Pacheco et al. 2017; Wang et al. 2016, 2018, 2019b; Cheng et al. 2016; Min and Xu 2016; Raza and Qamar 2017; Li et al. 2017; Das et al. 2017; Tiwari et al. 2018; Lin et al. 2018; Yao and Zhang 2017; Dai et al. 2018. As we know, attribute reduction plays an important role in data mining and knowledge discovery. Attribute reduction methods can be classified into non-incremental methods and incremental methods according to whether the computation of attribute reduction is from scratch or no
Data Loading...
