Assessment of Rough Set Theory in Relation to Risks Regarding Hydraulic Engineering Investment Decisions

Qu, Jihong; Bai, Xiao; Gu, Jun; Taghizadeh–Hesary, Farhad; Lin, Ji

doi:10.3390/math8081308

Cited by 13 publications

(6 citation statements)

References 18 publications

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“…In particular, the considered data set concerns eight project evaluations described using the attributes construction expense (CE), financial income (FI), strategy benefit (SB), and external influence (EI) in order to decide whether to make an investment (I), a delayed investment (DI) or no investment (NI) in each project. This data set was introduced in [30] and is represented in Table 7, where every unit corresponds to 10,000 yuan. This data set was analyzed from an RST perspective in [30] and was studied in the fuzzy framework in [13].…”

Section: A Toy Examplementioning

confidence: 99%

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Decision Making in Fuzzy Rough Set Theory

Chacón-Gómez,

Cornejo,

Medina

2023

Mathematics

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Decision rules are powerful tools to manage information and to provide descriptions of data sets; as a consequence, they can acquire a useful role in decision-making processes where fuzzy rough set theory is applied. This paper focuses on the study of different methods to classify new objects, which are not considered in the starting data set, in order to determine the best possible decision for them. The classification methods are supported by the relevance indicators associated with decision rules, such as support, certainty, and credibility. Specifically, the first one is based on how the new object matches decision rules that describe the data set, while the second one also takes into account the representativeness of these rules. Finally, the third and fourth methods take into account the credibility of the rules compared with the new object. Moreover, we have shown that these methods are richer alternatives or generalize other approaches given in the literature.

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Section: A Toy Examplementioning

confidence: 99%

“…This data set was introduced in [30] and is represented in Table 7, where every unit corresponds to 10,000 yuan. This data set was analyzed from an RST perspective in [30] and was studied in the fuzzy framework in [13]. Now, we use that study to obtain the decision rules considered to decide on the investment in a new project.…”

Section: A Toy Examplementioning

confidence: 99%

Decision Making in Fuzzy Rough Set Theory

Chacón-Gómez,

Cornejo,

Medina

2023

Mathematics

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“…Consequently, the decision-making system takes on the features of generalization and constitutes an effective and intelligent data processing tool. RST proposes to replace an imprecise concept with a pair of precise concepts, called the lower and upper approximation of this concept [69]. The difference between the upper and lower approximations is precisely the boundary area to which all cases belong that cannot be correctly classified on the basis of current knowledge.…”

Section: S U Av F =mentioning

confidence: 99%

Application of machine learning and rough set theory in lean maintenance decision support system development

Antosz¹,

Jasiulewicz-Kaczmarek²,

Paśko³

et al. 2021

Eksploatacja i Niezawodność – Maintenance and Reliability

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Lean maintenance concept is crucial to increase the reliability and availability of maintenance equipment in the manufacturing companies. Due the elimination of losses in maintenance processes this concept reduce the number of unplanned downtime and unexpected failures, simultaneously influence a company’s operational and economic performance. Despite the widespread use of lean maintenance, there is no structured approach to support the choice of methods and tools used for the maintenance function improvement. Therefore, in this paper by using machine learning methods and rough set theory a new approach was proposed. This approach supports the decision makers in the selection of methods and tools for the effective implementation of Lean Maintenance.

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“…Ye et al [18] proposed a novel multi-level coarse set model (MLRS) based on attribute value taxonomies (AVT) and a complete subtree promotion scheme for mining data with attribute value classification. Qu et al [19] mined the investment decision knowledge of water projects through rough set theory for investment risk assessment. Agarwal et al [20] used rough sets for the mining grinding process to investigate the effect of its various input parameters on the response.…”

Section: Introductionmentioning

confidence: 99%

A Knowledge Acquisition Method of Ship Coating Defects Based on IHQGA-RS

Bu¹,

Ji²,

Zhang³

et al. 2022

Coatings

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Coating defects are caused by a series of factors such as the improper operation of workers and the quality of the coating itself. At present, the coating process of all shipyards is inspected and recorded at a specific time after construction, which cannot prevent and control defects scientifically. As a result, coating quality decreases, and production costs increase. Therefore, this paper proposes a knowledge acquisition method based on a rough set (RS) optimized by an improved hybrid quantum genetic algorithm (IHQGA) to guide the ship-coating construction process. Firstly, the probability amplitude is determined according to the individual position of the population, and the adaptive value k is proposed to determine the rotation angle of the quantum gate. On this basis, the simulated annealing algorithm is combined to enhance the local search ability of the algorithm. Finally, the algorithm is applied to rough set attribute reduction to improve the efficiency and accuracy of rough set attribute reduction. The data of 600 painted examples of 210-KBC bulk carriers from a shipyard between 2015 and 2020 are randomly selected to test the knowledge acquisition method proposed in the paper and other knowledge acquisition methods. The results show that the IHQGA attribute approximate reduction algorithm proposed in this paper is the first to reach the optimal adaptation degree of 0.847, the average adaptation degree is better than other algorithms, and the average consumption time is about 10% less than different algorithms, so the IHQGA has more vital and more efficient seeking ability. The knowledge acquisition result based on the IHQGA optimization rough set has 20–50% fewer rules and 5–10% higher accuracy than other methods, and the industry experts have high recognition. The knowledge acquisition method of this paper is validated on a hull segment. The obtained results are consistent with the expert diagnosis results, indicating that the method proposed in this paper has certain practicability.

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Assessment of Rough Set Theory in Relation to Risks Regarding Hydraulic Engineering Investment Decisions

Cited by 13 publications

References 18 publications

Decision Making in Fuzzy Rough Set Theory

Decision Making in Fuzzy Rough Set Theory

Application of machine learning and rough set theory in lean maintenance decision support system development

A Knowledge Acquisition Method of Ship Coating Defects Based on IHQGA-RS

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