On modeling similarity and three-way decision under incomplete information in rough set theory

Luo, Junfang; Fujita, Hamido; Yao, Yiyu; Qin, Keyun

doi:10.1016/j.knosys.2019.105251

Cited by 57 publications

(5 citation statements)

References 34 publications

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“…This type of business decision-making requires approximations to manage incomplete data in an uncertain environment. 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%

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%

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

Singh

Koohang

et al. 2023

IMDS

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“…The set of states is denoted by

which implies whether an object belongs to

or does not belong to

, respectively, and a set of three actions

where

, and

show the three actions in classifying an element

for

, and

for

. The loss function, which is concerned to the risk or cost of actions in different states, is represented in a

matrix in Table 1 [ 39 ].…”

Section: Preliminariesmentioning

confidence: 99%

A Novel generalization of sequential decision-theoretic rough set model and its application

Shaheen,

Khan,

Ali

et al. 2024

Heliyon

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“…RST [62,63] has the ability to deal with classification problems of fuzzy and uncertain information, and it thus has been widely used in medical, financial, manufacturing, imaging processing, and other fields of different industries. In RST [26,64], the training sample and the corresponding attributes are regarded as an information system, a decision table is composed of all data, and the decision rule is based on "If conditional attribute then decision attribute" for if-then decision rules to represent.…”

Section: Research On Rough Set Theory and Its Related Applicationsmentioning

confidence: 99%

A Study to Identify Long-Term Care Insurance Using Advanced Intelligent RST Hybrid Models with Two-Stage Performance Evaluation

Chen

Hung

Lin³

2023

Mathematics

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With the motivation of long-term care 2.0 plans, forecasting models to identify potential customers of long-term care insurance (LTCI) are an important and interesting issue. From the limited literature, most past researchers emphasize traditional statistics techniques to address this issue; however, these are lacking in some areas. For example, intelligent hybrid models for LTCI are lacking, performance measurement of components for hybrid models is lacking, and research results for interpretative capacities are lacking, resulting in a black box scenario and difficulty in making decisions, and the gap between identifying potential customers and constructing hybrid models is unbridged. To solve the shortcomings mentioned above, this study proposes some advanced intelligent single and hybrid models; the study object is LTCI customers. The proposed hybrid models were used on the experimental dataset collected from real insurance data and possess the following advantages: (1) The feature selection technique was used to simplify variables for the purpose of improving model performance. (2) The performance of hybrid models was evaluated against some machine learning methods, including rough set theory, decision trees, multilayer perceptron, support vector machine, genetic algorithm, random forest, logistic regression, and naive Bayes, and sensitivity analysis was performed in terms of accuracy, coverage, rules number, and standard deviation. (3) We used the C4.5 algorithm of decision trees and the LEM2 algorithm of rough sets to extract and provide valuably comprehensible decisional rules as decision-making references for the interested parties for their varied benefits. (4) We used post hoc testing to verify the significant difference in groups. Conclusively, this study effectively identifies potential customers for their key attributes and creates a decision rule set of knowledge for use as a reference when solving practical problems by forming a structured solution. This study is a new trial in the LTCI application field and realizes novel creative application values. Such a hybrid model is rarely seen in identifying LTCI potential customers; thus, the study has sufficient application contribution and managerial benefits to attract much concern from the interested parties.

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On modeling similarity and three-way decision under incomplete information in rough set theory

Cited by 57 publications

References 34 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 Novel generalization of sequential decision-theoretic rough set model and its application

A Study to Identify Long-Term Care Insurance Using Advanced Intelligent RST Hybrid Models with Two-Stage Performance Evaluation

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