Fuzzy rough sets and fuzzy rough neural networks for feature selection: A review

Ji, Wanting; Pang, Yan; Jia, Xiaoyun; Wang, Zhongwei; Feng, Huanqing; Song, Baoyan; Liu, Mingzhe; Wang, Ruili

doi:10.1002/widm.1402

Cited by 40 publications

(17 citation statements)

References 113 publications

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“…Data mining technology based on rough set adopts association rules for statistics, which can find out the key attributes of teaching optimization reform (Ji et al, 2021). Blended learning will adjust the teaching path according to students’ learning interests.…”

Section: Optimization Of Blended Learning Model With Data Mining Algo...mentioning

confidence: 99%

“…Especially in colleges and universities, teachers have started to build online learning platforms suitable for their needs. Besides, scholars also study the meanings of autonomous learning in the information environment (Alraimi et al, 2015; Chen et al, 2013), construct some autonomous learning mode in the information environment (Hsieh, 2017; Xu et al, 2004), design autonomous learning courseware (Ji et al, 2021) and online learning system (Cotterall, 2000). Nevertheless, there is a gap between the development of technology and education technology application (Zou, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Improvement of Students’ Autonomous Learning Behavior by Optimizing Foreign Language Blended Learning Mode

Wang

Zhang

2022

Sage Open

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Given the significance of cultivating students’ autonomous learning ability, there is a need to develop an instructional model that can improve students’ awareness and behavior of autonomous learning, as well as to explore the effectiveness and optimization of this model effectively. Taking college English course as a case study, this paper constructs a blended learning mode based on SPOC, which combines advantages of online and offline teaching. 15 types of nonredundant sets resulting from 500 questionnaires has been explored, and the optimal factor combinations have been found out from 15 types with the technology of data mining to optimize the mode constructed previously. Optimized blended learning mode, emphasizing the optimal factors more, has been applied to College English curriculum design and teaching practice in China. Surveys of students’ achievement and autonomous learning behavior have been conducted after experiment. The results of the research indicate that the optimized blended learning mode will stimulate foreign language learners’ learning motivation, cultivate their autonomous learning ability, so as to construct and improve their autonomous learning behavior further.

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Section: Optimization Of Blended Learning Model With Data Mining Algo...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improvement of Students’ Autonomous Learning Behavior by Optimizing Foreign Language Blended Learning Mode

Wang

Zhang

2022

Sage Open

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“…In recent years, multisensor data fusion has developed rapidly; when dealing with situation fusion, many scholars also use nonprobabilistic fusion methods such as random set [132][133][134], rough set [135][136][137][138], fuzzy logic [139][140][141][142], and Dempster-Shafer [77,143,144] to achieve ideal results.…”

Section: Fuzzy Set Eorymentioning

confidence: 99%

A New View of Multisensor Data Fusion: Research on Generalized Fusion

Chen

Liu

et al. 2021

Mathematical Problems in Engineering

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Multisensor data generalized fusion algorithm is a kind of symbolic computing model with multiple application objects based on sensor generalized integration. It is the theoretical basis of numerical fusion. This paper aims to comprehensively review the generalized fusion algorithms of multisensor data. Firstly, the development and definition of multisensor data fusion are analyzed and the definition of multisensor data generalized fusion is given. Secondly, the classification of multisensor data fusion is discussed, and the generalized integration structure of multisensor and its data acquisition and representation are given, abandoning the research characteristics of object oriented. Then, the principle and architecture of multisensor data fusion are analyzed, and a generalized multisensor data fusion model is presented based on the JDL model. Finally, according to the multisensor data generalized fusion architecture, some related theories and methods are reviewed, and the tensor-based multisensor heterogeneous data generalized fusion algorithm is proposed, and the future work is prospected.

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“…Its inception provides a reliable theory base for the following research. So far, this theory has been investigated from the views of theory and practice, for example, a more general concept of a fuzzy rough set [14][15][16][17], the constructive definition of fuzzy rough approximation operators [18,19], feature selection [20][21][22], as well as a wide range of applications in attribute and data reduction [23,24], multi-attribute decision making [25], and so on. Unlike the fuzzy rough set, Liu [26] put forward a fuzzy rough variable (FRV) in 2002.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Rough Programming Models: An Expected Value Perspective

et al. 2022

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Usually, the quasi-normal fluctuations in practical applications are described via symmetric uncertainty variables, which is a common phenomenon in the manufacturing industry. However, it is relatively scarce in the literature to discuss two-fold uncertainty due to the its complexity. To deal with roughness and ambiguity to accommodate inherent uncertainties, fuzzy rough programming approaches are put forward. In this paper, we pay attention to exploring two kinds of programming problems, namely fuzzy rough single-objective programming and fuzzy rough multi-objective programming, in which objective functions and/or constraints involve fuzzy rough variables (FRV). In accordance with the related existing research of FRVs, such as the chance measure and the expected value (EV) operator, this paper further discusses the EV model, convexity theory, and the crisp equivalent model of fuzzy rough programming. After that, combined with the latest published NIA-S fuzzy simulation technique, a new fuzzy rough simulation algorithm is developed to calculate the EVs of complicated functions for handling the presented fuzzy rough programming problems. In the end, the two types of numerical examples are provided for demonstration.

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Fuzzy rough sets and fuzzy rough neural networks for feature selection: A review

Cited by 40 publications

References 113 publications

Improvement of Students’ Autonomous Learning Behavior by Optimizing Foreign Language Blended Learning Mode

Improvement of Students’ Autonomous Learning Behavior by Optimizing Foreign Language Blended Learning Mode

A New View of Multisensor Data Fusion: Research on Generalized Fusion

Fuzzy Rough Programming Models: An Expected Value Perspective

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