Incremental Learning Researches on Rough Set Theory: Status and Future

Incremental Learning Researches on Rough Set Theory: Status and Future

Dun Liu (School of Economics and Management, Southwest Jiaotong University, Chengdu, China) and Decui Liang (School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, China)
Copyright: © 2014 |Pages: 14
DOI: 10.4018/ijrsda.2014010107
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Abstract

Rough set theory is an effective tool to deal with information with uncertainty, and has been successfully applied in many fields. Incremental learning as an efficient strategy for data analysis in dynamic environment enables acquiring additional knowledge from new information by using prior knowledge and has drawn the widespread attentions of many scholars. In this paper, the authors discuss the status of incremental learning researches on rough sets and give potential future research directions. The authors first review basic concepts of rough sets and list three variations of information system in the dynamic decision procedures. Then, the authors investigate and summarize the corresponding incremental learning strategies for the three variations with different research viewpoints, respectively. Finally, the authors further tease out the research framework of our work and identify some future possible research directions.
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Introduction

As an effective mathematical tool, rough set theory (RST) proposed by Pawlak (1982), plays an important role in many fields of data analysis. In RST, it utilizes the lower and upper approximations to deal with an imprecise and uncertainty information (concept), and has been successfully applied in many domains, such as water demand prediction (An et al., 1996), business failure prediction (Dimitras et al., 1999), cluster analysis (Lingras et al., 2010), airline service strategies (Liou et al., 2010), etc. Based on the two approximations, the main researches of RST focus on three aspects, the algebraic structure with two approximation operators (Yao, 1996, 1998); attribute reduction (feature selection) (Dey et al., 2011; Liang et al., 2013; Wang et al., 2013a; Wang et al., 2013b; Xu et al., 2011) and decision rule induction (Blaszczynski & Slowinski, 2003; Fan et al., 2009; Greco et al., 2004; Liang, 2010; Shan & Ziarko, 1992).

Unfortunately, the existing researches on rough sets mainly involve a static environment. In the real life, we may face to a dynamic decision environment (Michalski, 1985). For example, a company always encounters some employees leave or recruits this company. In order to keep the pace of produce process, the employer (manager) need decide how to employ in real time. In addition, accompanied by cheaper storage, evolution of digital data and information collection devices, such as cell phones, laptops, and sensors, the data in today’s is getting increasingly larger and need real-time processing (Demirkan & Delen, 2013). With an overwhelming amount of data arriving at a terabyte and even exabyte scale, the big data (or the massive data) becomes one of the biggest challenges in knowledge discovery. In the context of big data in dynamic environments, the existing information processing technologies based on rough sets should be extended to suit for the updating data (Chen et al., 2012a).

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