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Complex Decision Modeling Framework with Fairly Operators and Quaternion Numbers under Intuitionistic Fuzzy rough Context

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1893-1932页

作者： Nadeem Salamat Muhammad Kamran Shahzaib Ashraf Manal Elzain Mohammed Abdulla Rashad Ismail Mohammed M.Al-Shamiri Institute of Mathematics Khwaja Fareed University of Engineering&Information TechnologyRahim Yar Khan64200Pakistan Department of Mathematics Thal University BhakkarPunjab30000Pakistan Department of Mathematics Faculty of Science and ArtsKing Khalid UniversityMuhayl Assir61913Saudi Arabia

The main goal of informal computing is to overcome the limitations of hypersensitivity to defects and uncertainty while maintaining a balance between high accuracy,accessibility,and cost-effectiveness.This paper investigates the potential applications of intuitionistic fuzzy sets(IFS)with rough sets in the context of sparse data.When it comes to capture uncertain information emanating fromboth upper and lower approximations,these intuitionistic fuzzy rough numbers(IFRNs)are superior to intuitionistic fuzzy sets and pythagorean fuzzy sets,respectively.We use rough sets in conjunction with IFSs to develop several fairly aggregation operators and analyze their underlying properties.We present numerous impartial laws that incorporate the idea of proportionate dispersion in order to ensure that the membership and non-membership activities of IFRNs are treated equally within these principles.These operations lead to the development of the intuitionistic fuzzy rough weighted fairly aggregation operator(IFRWFA)and intuitionistic fuzzy rough ordered weighted fairly aggregation operator(IFRFOWA).These operators successfully adjust to membership and non-membership categories with fairness and subtlety.We highlight the unique qualities of these suggested aggregation operators and investigate their use in the multiattribute decision-making field.We use the intuitionistic fuzzy rough environment’s architecture to create a novel strategy in situation involving several decision-makers and non-weighted data.Additionally,we developed a novel technique by combining the IFSs with quaternion numbers.We establish a unique connection between alternatives and qualities by using intuitionistic fuzzy quaternion numbers(IFQNs).With the help of this framework,we can simulate uncertainty in real-world situations and address a number of decision-making problems.Using the examples we have released,we offer a sophisticated and systematically constructed illustrative scenario that is intricately woven with the complexity ofmedical evaluation in

关键词： Intuitionistic fuzzy set quaternion numbers fuzzy logic decision-making rough set

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Eco-geographical Regionalization of China:An Approach Using the rough set Method

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Chinese Geographical Science 2022年第1期32卷 93-109页

作者： DENG Haoyu WU Shaohong YIN Yunhe GAO Jiangbo ZHAO Dongsheng Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China College of Resources and Environment University of Chinese Academy of SciencesBeijing 100049China

Eco-geographical regionalization involves dividing land into regions by considering both intra-regional consistency and interregional disparity and is based on the pattern of differentiation of eco-geographical elements.Owing to the complexity of the land surface,and the limitation of data and appropriate methods,regions in China have hitherto been mapped manually,meaning that the process of mapping was non-repeatable.To make the regionalization technique repeatable,this study aimed to extract and quantify the expert knowledge of regionalization using an automated method.The rough set method was adopted to extract rules of regionalization based on the existing eco-geographical regionalization map of China,as well as its corresponding meteorological and geological datasets.Then,the rules for regionalization were obtained hierarchically for each natural domain,each temperature zone,and each humidity region.Owing to differences in zonal differentiation,the rule extraction sequence for the eastern monsoon zone and Tibetan Alpine zone was temperature zone first followed by humidity region,with the reverse order being applied for the northwest arid/semi-arid zone.Results show that the extracted indicators were similar to those of the existing(expert-produced)regionalization scheme but more comprehensive.The primary indicator for defining temperature zones was the≥10℃ growing season,and the secondary indicators were the January and July mean temperatures.The primary and secondary indicators for identifying humid regions were aridity index and precipitation,respectively.Eco-geographical regions were mapped over China using these rules and the gridded indicators.Both the temperature zones and humidity regions mapped by the rules show≥85%consistency with the existing regionalization,which is higher than values for mapping by the commonly used simplified method that uses the classification of one indicator.This study demonstrates that the proposed rough set method can establish eco-geographical regionalization that is quant

关键词： Eco-geographical regionalization boundary rough set China

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rough set Model for Cognitive Expectation Embedded Interval-Valued Decision Systems

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Chinese Journal of Electronics 2018年第4期27卷 675-679页

作者： DAI Jianhua LIU Zhenbo HU Hu SHI Hong School of Computer Science and Technology Tianjin University College of Computer Science and Technology Zhejiang University

Interval-valued information system is a kind of knowledge representation model for uncertain information. An interval-valued attribute has an expectation by experience or background knowledge, called cognitive expectation. There are few studies aiming at intervalvalued attributes with cognitive expectations. We propose the concept of Interval-valued decision system with expectations(IDSE). A new dominance relation based on the distances between expectations and interval values is constructed. Based on the constructed dominance relation, a rough set model for IDSE is investigated. Attribute reduction in IDSE is also examined by using discernibility matrices and discernibility functions.

关键词： rough set Interval-valued decision system with cognitive expectation Attribute reduction

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Characteristics Prediction Method of Electro-hydraulic Servo Valve Based on rough set and Adaptive Neuro-fuzzy Inference System

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Chinese Journal of Mechanical Engineering 2010年第2期23卷 200-208页

作者： JIA Zhenyuan MA Jianwei WANG Fuji LIU Wei Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education Dalian University of Technology Dalian 116024 China

Synthesis characteristics of the electro-hydraulic servo valve are key factors to determine eligibility of the hydraulic production. Testing all synthesis characteristics of the electro-hydraulic servo valve after assembling leads to high repair rate and reject rate, so accurate prediction for the synthesis characteristics in the industrial production is particular important in decreasing the repair rate and the reject rate of the product. However, the research in forecasting synthesis characteristics of the electro-hydraulic servo valve is rare. In this work, a hybrid prediction method was proposed based on rough set（RS） and adaptive neuro-fuzzy inference system（ANFIS） in order to predict synthesis characteristics of electro-hydraulic servo valve. Since the geometric factors affecting the synthesis characteristics of the electro-hydraulic servo valve are from workers＇ experience, the inputs of the prediction method are uncertain. RS-based attributes reduction was used as the preprocessor, and then the exact geometric factors affecting the synthesis characteristics of the electro-hydraulic servo valve were obtained. On the basis of the exact geometric factors, ANFIS was used to build the final prediction model. A typical electro-hydraulic servo valve production was used to demonstrate the proposed prediction method. The prediction results showed that the proposed prediction method was more applicable than the artificial neural networks（ANN） in predicting the synthesis characteristics of electro-hydraulic servo valve, and the proposed prediction method was a powerful tool to predict synthesis characteristics of the electro-hydraulic servo valve. Moreover, with the use of the advantages of RS and ANFIS, the highly effective forecasting framework in this study can also be applied to other problems involving synthesis characteristics forecasting.

关键词： characteristics prediction rough set adaptive neuro-fuzzy inference system electro-hydraulic servo valve,artificial neural networks

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A Heuristic Approach for Context-Aware Recommendation Using rough set Theory

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Chinese Journal of Electronics 2018年第3期27卷 500-506页

作者： HE Ming DONG Tao LIU Yi College of Computer Science Beijing University of Technology

Context-aware recommender systems, aiming to further improve performance accuracy and user satisfaction by fully utilizing contextual information, have recently become one of the hottest topics in the domain of recommender systems. However, not all contextual information might be relevant or useful for recommendation purposes, and little work has been done on measuring how important the contextual information for recommendation.We propose a heuristic optimization algorithm based on rough set theory and collaborative filtering to using contextual information more efficiently for boosting recommendation. Our approach involves three processes. First, significant attributes to represent contextual information are extracted and measured to identify recommended items using rough set theory. Second, the user similarity is evaluated in a target context consideration. Third, collaborative filtering is applied to recommend appropriate items.We perform an empirical comparison of three approaches on two real-world data sets. The experimental results show that the proposed approach generates more accurate predictions.

关键词： Recommendation Context Context-aware recommendation Collaborative filtering rough set

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Multi-hierarchical fuzzy judgment and nested dominance relation of rough set theory-based environmental risk evaluation for tailings reservoirs

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Journal of Central South University 2015年第12期22卷 4797-4806页

作者：田森陈建宏 School of Resources and Environmental Science Chongqing University School of Resources and Safety Engineering Central South University School of Civil Engineering The University of Queensland

Environmental risk assessment of tailings reservoir assessment system is complex and has many index factors.In order to accurately judge surrounding environmental risks of tailings reservoirs and determinate the corresponding prevention and control work,multi-hierarchical fuzzy judgment and nested dominance relation of rough set theory are implemented to evaluate them and find out the rules of this evaluation system with 14 representative cases.The methods of multi-hierarchical fuzzy evaluation can overall consider each influence factor of risk assessment system and their mutual impact,and the index weight based on the analytic hierarchy process is relatively reasonable.rough set theory based on dominance relation reduces each index attribute from the top down,largely simplifies the complexity of the original evaluation system,and considers the preferential information in each index.Furthermore,grey correlation theory is applied to analysis of importance of each reducted condition attribute.The results demonstrate the feasibility of the proposed safety evaluation system and the application potential.

关键词： environmental risk tailings reservoir fuzzy evaluation dominance relation rough set grey correlation theory

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A generalized rough set-based information flling technique for failure analysis of thruster experimental data

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Chinese Journal of Aeronautics 2013年第5期26卷 1182-1194页

作者： Han Shan Zhu Qiang Li Jianxun Chen Lin Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China

Interval-valued data and incomplete data are two key problems for failure analysis of thruster experimental data and have been basically solved by the proposed methods in this paper. Firstly, information data acquired from the simulation and evaluation system formed as intervalvalued information system （IIS） is classified by the interval similarity relation. Then, as an improvement of the classical rough set, a new kind of generalized information entropy called ＂H＇-information entropy＂ is suggested for the measurement of uncertainty and the classification ability of IIS. There is an innovative information filling technique using the properties of H＇-information entropy to replace missing data by some smaller estimation intervals. Finally, an improved method of failure analysis synthesized by the above achievements is presented to classify the thruster experimental data, complete the information, and extract the failure rules. The feasibility and advantage of this method is testified by an actual application of failure analysis, whose performance is evaluated by the quantification of E-condition entropy.

关键词： Data acquisition Data classification Failure analysis Information filling rough set

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New method for discretization of continuous attributes in rough set theory

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Journal of Systems Engineering and Electronics 2010年第2期21卷 250-253页

作者： Rong Cong Xiukun Wang Kai Li Nanhai Yang School of Electronic and Information Engineering Dalian University of Technology Dalian 116024 E R. China Education Technology Center Dalian Naval Academy Dalian 116018 P. R. China The 92538 Unit of PLA Dalian 116041 E R. China

A new method for discretization of continuous attributes is put forward to overcome the limitation of the traditional rough sets, which cannot deal with continuous attributes.The method is based on an improved algorithm to produce candidate cut points and an algorithm of reduction based on variable precision rough information entropy. With the guarantee of consistency of decision system, the method can reduce the number of cut points and im- prove efficiency of reduction. Adopting variable precision rough information entropy as measure criterion, it has a good tolerance to noise. Experiments show that the algorithm yields satisfying reduction results.

关键词： rough set discretization reduction

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Method to determine α in rough set model based on connection degree

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Journal of Systems Engineering and Electronics 2009年第1期20卷 98-105页

作者： Li Huaxiong Zhou Xianzhong Huang Bing School of Management and Engineering Nanjing Univ. Nanjing 210093 P. R. China State Key Lab. for Novel Software Technology Nanjing Univ. Nanjing 210093 P. R. China School of Information Science Nanjing Audit Univ. Nanjing 210029 P. R. China

An improvement of tolerance relation is proposed in regard to rough set model based on connection degree by which reflexivity of relation can be assured without loss of information. Then, a method to determine optimal identity degree based on relative positive region is proposed so that the identity degree can be computed in an objective method without any preliminary or additional information about data, which is consistent with the notion of objectivity in rough set theory and data mining theory. Subsequently, an algorithm is proposed, and in two examples, the global optimum identity degree is found out. Finally, in regard to optimum connection degree, the method of rules extraction for connection degree rough set model based on generalization function is presented by which the rules extracted from a decision table are enumerated.

关键词： rough set data mining connection degree identity degree set pair relative positive region.

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Attribute Reduction with rough set in Context-Aware Collaborative Filtering

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Chinese Journal of Electronics 2017年第5期26卷 973-980页

作者： HE Ming REN Wanpeng College of Computer Science Beijing University of Technology

The problem of different contextual information to influence the user-item-context interactions at varying degrees in context-aware recommender systems is addressed.To improve the performance accuracy,we develop a novel attribute reduction algorithm in order to effectively extract the core contextual information using rough set.We combine collaborative filtering with contextual information significance to generate more accurate predictions.We experimentally evaluate our approach on UCI machine learning repository and two real world data sets.Experimental results demonstrate that our proposed approach outperforms existing state-of-theart context-aware recommendation methods.

关键词： Context-aware recommendation systems(CARS) Collaborative filtering rough set

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