Methods and Practices of Three-Way Decisions for Complex Problem Solving

Yu, Hong; Wang, Guoyin; Hu, Baoqing; Jia, Xiuyi; Li, Huaxiong; Li, Tianrui; Liang, Decui; Liang, Jiye; Liu, Baoxiang; Liu, Dun; Ma, Jian; Miao, Duoqian; Min, Fan; Qi, Jianjun; Shang, Lin; Xu, Jiucheng; Yang, Hai-Long; Yao, JingTao; Yao, Yiyu; Zhang, Hongying; Zhang, Yanping; Zhu, Yanhui

doi:10.1007/978-3-319-25754-9_23

Cited by 14 publications

(3 citation statements)

References 46 publications

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“…Thus, Professor Yao [6] proposed the three-way decision theory to overcome some drawbacks of binary-decisions. The essential ideas of three-way decision theory is that to divide a universe into three disjoint parts and process the different part using different strategy [5]. Therefore, in this paper, we use the three-way decision theory to deal with the uncertainty of detecting concept drift and we are able to make new and unique contributions to concept drift detection and applications.…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, a theory of three-way decisions is proposed as an extension of the commonly used binary-decision model with an added third option [6]. In the last few years, we have witnessed a fast growing development and applications of three-way approaches in areas of decision making [7], email spam filtering [8], three-way investment decisions [9], three-way cluster analysis [10,11] and many others [5]. Their preliminary results provide us with a tool for studying the problem of detecting concept drift.…”

Section: Introductionmentioning

confidence: 99%

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A Tree-based Concept Drift Detection Method by Three-way Decisions

Su¹

2017

Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 201

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Abstract. Concept drift detection is an active research area in data stream mining. The existing concept drift detection methods usually determine a concept is drifted or not drifted. In other words, the concepts are assigned into two classes such as drifted and un-drifted, which is typically based on two-way decisions. Most likely, these methods incorrectly determine that concept drift occurs due to some uncertain factors such as noise, and some real concept drifts are not detected. Inspired by the three-way decision theory, we propose a tree-based concept drift detection method by three-way decisions in this paper, in order to improve the accuracy of detection. The basic idea of the method is to assign the concepts into three classes such as drifted, nondeterministic drifted and un-drifted. Furthermore, concepts in the class of nondeterministic drifted are determined further according to the deviation between the classification error rates. The results of comparison experiments show the effectiveness and efficiency of the proposed methods.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Tree-based Concept Drift Detection Method by Three-way Decisions

Su¹

2017

Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 201

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show abstract

“…The theory of three-way decisions (3WD) proposed by Yao is an extension of classic two-way decisions (2WD) [41][42][43][44][45], whose basic idea comes from Pawlak's rough sets [28] and probability rough sets proposed by Yao [38][39][40][41][42][43][44][45][46]. In recent years, researches on three-way decisions have been paid close attention by a growing number of scholars and are mainly reflected in the following three aspects [9,10,15,47].…”

Section: Introductionmentioning

confidence: 99%