Multigranulation consensus fuzzy-rough based attribute reduction

Ding, Weiping; Wang, Jiandong; Wang, Jiehua

doi:10.1016/j.knosys.2020.105945

Cited by 23 publications

(8 citation statements)

References 38 publications

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“…The application of rough set theory in driving decision rules from incomplete data is well-reported in the literature (Luo et al ., 2020; Meng and Xu, 2023). The rough set-based approximation is used in various business decision-making, such as selecting factors for product quotation (Meng and Xu, 2023), uncertainty-oriented credit scoring (Wu et al ., 2022a, b, c, d) and attribute reduction for dynamic big data classification tasks in medical decision support systems neonatal brain disorders (Ding et al ., 2020).…”

Section: Resultsmentioning

confidence: 99%

“…scoring (Wu et al, 2022a, b, c, d) and attribute reduction for dynamic big data classification tasks in medical decision support systems neonatal brain disorders (Ding et al, 2020). Topic 10-"Scheduling and Optimization in Production" represents the research related to scheduling problems in business domains such as production and manufacturing (Leng et al, 2022;Mahmud et al, 2022).…”

Section: Soft Computing In Businessmentioning

confidence: 99%

“…However, it is difficult to choose an appropriate set of rules with which to get accurate solutions using fuzzy set theory (Llopis-Albert et al, 2022). (continued ) • To develop advanced multi-granulation fuzzy-rough attribute reduction techniques to improve the efficiency and robustness of human cognitive processes and decision-making (Ding et al, 2020). • To develop the framework that focuses on identifying optimal threshold values for approximation degree in incomplete covering rough sets, as these have a direct impact on the efficiency of data reduction (Meng and Xu, 2023).…”

Section: Soft Computing In Businessmentioning

confidence: 99%

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Soft computing in business: exploring current research and outlining future research directions

Singh

Koohang

et al. 2023

IMDS

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PurposeThe primary aim of this study is to detail the use of soft computing techniques in business and management research. Its objectives are as follows: to conduct a comprehensive scientometric analysis of publications in the field of soft computing, to explore the evolution of keywords, to identify key research themes and latent topics and to map the intellectual structure of soft computing in the business literature.Design/methodology/approachThis research offers a comprehensive overview of the field by synthesising 43 years (1980–2022) of soft computing research from the Scopus database. It employs descriptive analysis, topic modelling (TM) and scientometric analysis.FindingsThis study's co-citation analysis identifies three primary categories of research in the field: the components, the techniques and the benefits of soft computing. Additionally, this study identifies 16 key study themes in the soft computing literature using TM, including decision-making under uncertainty, multi-criteria decision-making (MCDM), the application of deep learning in object detection and fault diagnosis, circular economy and sustainable development and a few others.Practical implicationsThis analysis offers a valuable understanding of soft computing for researchers and industry experts and highlights potential areas for future research.Originality/valueThis study uses scientific mapping and performance indicators to analyse a large corpus of 4,512 articles in the field of soft computing. It makes significant contributions to the intellectual and conceptual framework of soft computing research by providing a comprehensive overview of the literature on soft computing literature covering a period of four decades and identifying significant trends and topics to direct future research.

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Section: Resultsmentioning

confidence: 99%

Section: Soft Computing In Businessmentioning

confidence: 99%

Section: Soft Computing In Businessmentioning

confidence: 99%

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Soft computing in business: exploring current research and outlining future research directions

Singh

Koohang

et al. 2023

IMDS

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“…Therefore, human subjectivity will inevitably be a factor in emergency decision-making. To avoid the decision error caused by fuzziness and uncertainty, a wide variety of information expression methods have been proposed to capture DMs' subjectivity, such as the intuitionistic [7,8], hesitant [9,10], and Pythagorean fuzzy sets [11], among others.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Attribute Decision-Making Method Using Belief-Based Probabilistic Linguistic Term Sets and Its Application in Emergency Decision-Making

Liu¹,

Fei²,

Mi³

2023

Computer Modeling in Engineering &Amp; Sciences

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Probabilistic linguistic term sets (PLTSs) are an effective tool for expressing subjective human cognition that offer advantages in the field of multi-attribute decision-making (MADM). However, studies have found that PLTSs have lost their ability to accurately capture the views of decision-makers (DMs) in certain circumstances, such as when the DM hesitates between multiple linguistic terms or the decision information is incomplete, thus affecting their role in the decision-making process. Belief function theory is a leading stream of thought in uncertainty processing that is suitable for dealing with the limitations of PLTS. Therefore, the purpose of this study is to extend PLTS to incorporate belief function theory. First, we provide the basic concepts of the extended PLTS (i.e., belief-based PLTS) through case analyses. Second, the aggregation operator of belief-based PLTS is defined with the ordered weighted average (OWA)-based soft likelihood function, which is improved by considering the reliability of the information source. Third, to measure the magnitude of different belief-based PLTSs, the belief interval of singleton is calculated, and the comparison method of belief-based PLTS is constructed based on probabilities. On the basis of the preceding discussion, we further develop an emergency decision framework that includes several novel techniques, such as attribute weight determination and decision information aggregation. Finally, the usefulness of the framework is demonstrated through a case study, and its effectiveness is illustrated through a series of comparisons.

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“…Granular computing has gradually developed into a new branch in the field of artificial intelligence. The granular computing models have been proposed include: rough set [5], fuzzy set [6], fuzzy rough set [7], three-way decision [8], etc. Rough set is one of the granular computing models, which can deal with and analyze the uncertain information in the decision-making process.…”

mentioning

confidence: 99%

A Novel Spark-Based Attribute Reduction and Neighborhood Classification for Rough Evidence

Ding

Sun

et al. 2024

IEEE Trans. Cybern.

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Neighborhood classification (NEC) algorithms has been widely used to solve classification problems. Most traditional NEC algorithms employ the majority voting mechanism as the basis for final decision-making. However, this mechanism hardly considers the spatial difference and label uncertainty of the neighborhood samples, which may increase the possibility of the misclassification. In addition, the traditional NEC algorithms need to load the whole data into memory at once, which is computationally inefficient when the size of data set is large. To address these problems, we propose a novel Spark-based attribute reduction and neighborhood classification for rough evidence in this paper. Specifically, we first construct a multi-granular sample space using parallel undersampling method. Then, we evaluate the significance of attribute by neighborhood rough evidence decision error rate and remove the redundant attribute on different samples subspaces. Based on this attribute reduction algorithm, we design a parallel attribute reduction algorithm which is able to compute equivalence classes in parallel and parallelize the process of searching for candidate attributes. Finally, we introduce the rough evidence into the classification decision of traditional NEC algorithms and parallelize the classification decision process. Furthermore, the proposed algorithms are conducted in the Spark parallel computing framework. Experimental results on both small and large-scale data sets show that the proposed algorithms outperform the benchmarking algorithms in the classification accuracy and the computational efficiency.

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Multigranulation consensus fuzzy-rough based attribute reduction

Cited by 23 publications

References 38 publications

Soft computing in business: exploring current research and outlining future research directions

Soft computing in business: exploring current research and outlining future research directions

A Multi-Attribute Decision-Making Method Using Belief-Based Probabilistic Linguistic Term Sets and Its Application in Emergency Decision-Making

A Novel Spark-Based Attribute Reduction and Neighborhood Classification for Rough Evidence

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