A rough set theory application in forecasting models

Sharma, Haresh Kumar; Kumari, Kriti; Kar, Samarjit

doi:10.31181/dmame2003001s

Cited by 32 publications

(16 citation statements)

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“…Content may change prior to final publication. Moreover, having a lower percentage of MAPE indicates a high degree of accuracy, for which it can be said that the smaller the value MAPE, the better the forecast [36].…”

Section: Fig 2 Explains Each Configuration Of Dhnn-ran3satmentioning

confidence: 99%

Random Satisfiability: A Higher-Order Logical Approach in Discrete Hopfield Neural Network

et al. 2021

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A conventional systematic satisfiability logic suffers from a nonflexible logical structure that leads to a lack of interpretation. To resolve this problem, the advantage of introducing nonsystematic satisfiability logic is important to improve the flexibility of the logical structure. This paper proposes Random 3 Satisfiability (RAN3SAT) with three types of logical combinations (k =1, 3, k =2, 3, and k =1, 2, 3) to report the behaviors of multiple logical structures. The different types of RAN3SAT enforced with Discrete Hopfield Neural Network (DHNN) are included with benchmark searching techniques, such as Exhaustive Search algorithm. Additionally, to strengthen and certify the behavior of the proposed model, we extensively conducted several performance evaluation metrics with a specific number of neurons. In particular, the experimental results revealed that RAN3SAT was able to be implemented in DHNN, and each logical combination has its characteristics. Nonetheless, RAN3SAT provides more neuron variations in the whole solution space. The proposed model can also be applied in real-world applications such as the logic mining approach since RAN3SAT consists of various logic combinations that behave as input language to transform raw data into informative output.

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Section: Fig 2 Explains Each Configuration Of Dhnn-ran3satmentioning

confidence: 99%

Random Satisfiability: A Higher-Order Logical Approach in Discrete Hopfield Neural Network

et al. 2021

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“…In the future studies, we will extend the OWA operator to process rough sets (Sharma et al, , 2020 and its applications in multicriteria evaluation (Roy et al, 2019;…”

Section: Discussionmentioning

confidence: 99%

Determine OWA operator weights using kernel density estimation

Lin

et al. 2020

Economic Research-Ekonomska Istraživanja

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Some subjective methods should divide input values into local clusters before determining the ordered weighted averaging (OWA) operator weights based on the data distribution characteristics of input values. However, the process of clustering input values is complex. In this paper, a novel probability density based OWA (PDOWA) operator is put forward based on the data distribution characteristics of input values. To capture the local cluster structures of input values, the kernel density estimation (KDE) is used to estimate the probability density function (PDF), which fits to the input values. The derived PDF contains the density information of input values, which reflects the importance of input values. Therefore, the input values with high probability densities (PDs) should be assigned with large weights, while the ones with low PDs should be assigned with small weights. Afterwards, the desirable properties of the proposed PDOWA operator are investigated. Finally, the proposed PDOWA operator is applied to handle the multicriteria decision making problem concerning the evaluation of smart phones and it is compared with some existing OWA operators. The comparative analysis shows that the proposed PDOWA operator is simpler and more efficient than the existing OWA operators.

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“…Roy et al [23] introduced a rough strength relational decision making trial and evaluation laboratory (DEMATEL) model for analyzing the key success factors of hospital service quality. Sharma et al [24] introduced a rough set theory application in forecasting models.…”

Section: Literature Reviewmentioning

confidence: 99%

Linear Diophantine Fuzzy Soft Rough Sets for the Selection of Sustainable Material Handling Equipment

et al. 2020

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The concept of linear Diophantine fuzzy sets (LDFSs) is a new approach for modeling uncertainties in decision analysis. Due to the addition of reference or control parameters with membership and non-membership grades, LDFS is more flexible and reliable than existing concepts of intuitionistic fuzzy sets (IFSs), Pythagorean fuzzy sets (PFSs), and q-rung orthopair fuzzy sets (q-ROFSs). In this paper, the notions of linear Diophantine fuzzy soft rough sets (LDFSRSs) and soft rough linear Diophantine fuzzy sets (SRLDFSs) are proposed as new hybrid models of soft sets, rough sets, and LDFS. The suggested models of LDFSRSs and SRLDFSs are more flexible to discuss fuzziness and roughness in terms of upper and lower approximation operators. Certain operations on LDFSRSs and SRLDFSs have been established to discuss robust multi-criteria decision making (MCDM) for the selection of sustainable material handling equipment. For these objectives, some algorithms are developed for the ranking of feasible alternatives and deriving an optimal decision. Meanwhile, the ideas of the upper reduct, lower reduct, and core set are defined as key factors in the proposed MCDM technique. An application of MCDM is illustrated by a numerical example, and the final ranking in the selection of sustainable material handling equipment is computed by the proposed algorithms. Finally, a comparison analysis is given to justify the feasibility, reliability, and superiority of the proposed models.

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A rough set theory application in forecasting models

Cited by 32 publications

References 0 publications

Random Satisfiability: A Higher-Order Logical Approach in Discrete Hopfield Neural Network

Random Satisfiability: A Higher-Order Logical Approach in Discrete Hopfield Neural Network

Determine OWA operator weights using kernel density estimation

Linear Diophantine Fuzzy Soft Rough Sets for the Selection of Sustainable Material Handling Equipment

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