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Three-Way Decision in a multi-source information system and Its Applications

IEEE ACCESS 2019年 7卷 108343-108359页

作者： Luo, Sheng Guangxi Univ Finance & Econ Sch Business Adm Nanning 530003 Peoples R China Xi An Jiao Tong Univ Sch Econ & Finance Xian 710049 Shaanxi Peoples R China

Three-way decision (3WD) is a decision that conforms to human's cognitive pattern and offers a new visual angle for solving practical decision-making problems. An information system (IS) represents relationships between objects and attributes. And a multi-source information system (MSIS) is an IS which composes of multi-source data sets. This paper presents 3WD in an MSIS as well as gives its application in petroleum project investment. Considering that risks are uncertain, loss functions are first given by means of interval numbers. Then, multi-granulation decision-theoretic rough set (MGDTRS) models in a given MSIS are proposed from the point of view of multi-granulation. Next, based on the idea of a decision-theoretic rough set (DTRS), the optimistic and pessimistic 3WDs in the MSIS are proposed. Finally, an example of petroleum project investment is used to support the feasibility of the proposed decision method.

关键词： Three-way decision multi-source information system multi-granularity decision-theoretic rough set loss function interval number petroleum project investment

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Generalized multi-granulation double-quantitative decision-theoretic rough set of multi-source information system

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2019年 115卷 157-179页

作者： Sang, Binbin Yang, Lei Chen, Hongmei Xu, Weihua Guo, Yanting Yuan, Zhong Southwest Jiaotong Univ Sch Informat Sci & Technol Inst Artificial Intelligence Chengdu 611756 Sichuan Peoples R China Southwest Jiaotong Univ Natl Engn Lab Integrated Transportat Big Data App Chengdu 611756 Sichuan Peoples R China Chongqing Univ Technol Sch Sci Chongqing 400054 Peoples R China Southwest Univ Coll Artificial Intelligence Chongqing 400715 Peoples R China Macau Univ Sci & Technol Fac Informat Technol Taipa Macao Peoples R China

Traditionally, multi-source information system (MsIS) is typically integrated into a single information table for knowledge acquisition. Therefore, discovering knowledge directly from MsIS without information loss is a valuable research direction. In this paper, we propose the generalized multi-granulation double-quantitative decision-theoretic rough set of multi-source information system (MS-GMDQ-DTRS) to handle this issue. First, we propose a generalized multi-granulation rough set model for MsIS (MS-GMRS) as the basis of other models. In this model, each single information system is treated as a granular structure. Next, we combine MS-GMRS with double-quantitative decision-theoretic rough set to obtain two new models. They have better fault tolerance capability compared with MS-GMRS. Furthermore, we propose corresponding algorithms to calculate the approximation accuracy of the proposed models. Experiments are carried out on four datasets downloaded from UCI. Experimental results show that the two new models have better fault tolerance in directly acquiring knowledge from MsIS. (C) 2019 Elsevier Inc. All rights reserved.

关键词： multi-source information system Decision theoretic rough set Double-quantitative rough set Generalized multi-granulation rough set model Approximation accuracy

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Dynamic information fusion in multi-source incomplete interval-valued information system with variation of information sources and attributes

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information SCIENCES 2022年 608卷 1-27页

作者： Zhang, Xiaoyan Chen, Xiuwei Xu, Weihua Ding, Weiping Southwest Univ Coll Artificial Intelligence Chongqing 400715 Peoples R China Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China

Interval-valued data describe the random phenomenon that abounds in the real world, a pivotal research orientation in uncertainty processing. With the rapid development of big data, we may gather information from multiple information sources. To effectively acquire knowledge from multiple information sources, information fusion is commonly used to get a unified representation. However, sometimes data gathered from multiple sources may be lost;it is meaningful and necessary to study the fusion of multi-source incomplete interval-valued data. We propose a novel information fusion method based on information entropy for multi-source incomplete interval-valued data and four incre-mental fusion mechanisms characterized by the change in information sources and attri-butes. The corresponding static and dynamic fusion algorithms are designed, and their time complexities are analyzed. Experimental results show that the proposed method out-performs the mean, max, and min fusion methods. Furthermore, the four incremental fusion mechanisms reduced the runtime compared with the static fusion mechanism.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Dynamic fusion information entropy Incomplete interval-valued multi-source information system

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An unsupervised feature extraction and fusion framework for multi-source data based on copula theory

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2025年 180卷

作者： Chen, Xiuwei Lai, Li Luo, Maokang Sichuan Univ Sch Math Chengdu 610064 Peoples R China

With the development of big data technology, people are increasingly facing the challenge of dealing with massive amounts of multi-source or multi-sensor data. Therefore, it becomes crucial to extract valuable information from such data. information fusion techniques provide effective solutions for handling multi-source data and can be categorized into three levels: data-level fusion, feature-level fusion, and decision-level fusion. Feature-level fusion combines features from multiple sources to create a consolidated feature, enhancing information richness. This paper proposes an unsupervised feature extraction and fusion method for multi-source data that utilizes the R-Vine copula, denoted as CF. The method starts by performing kernel density estimation to extract each data source's marginal density and distribution. Next, the maximum spanning tree is employed to select a vine structure for each attribute, and the corresponding copulas are chosen using maximum likelihood estimation and the AIC criterion. The joint probability density of each attribute across all information sources can be obtained by utilizing the relevant vine structure and copulas, serving as the final fusion feature. Finally, the proposed method is evaluated on eighteen simulated datasets and six real datasets. The results indicate that compared to several state-of-theart fusion methods, the CF method can significantly enhance the classification accuracy of popular classifiers such as KNN, SVM, and Logistic Regression.

关键词： Copula theory Feature fusion Feature extraction multi-source information system

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A Novel multi-source information Fusion Method Based on Dependency Interval

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第4期8卷 3180-3194页

作者： Xu, Weihua Lin, Yufei Wang, Na Southwest Univ Coll Artificial Intelligence Chongqing 400715 Peoples R China Kaishu Storytelling Co Big Data Dept Beijing 100012 Peoples R China

With the rapid development of Big Data era, it is necessary to extract necessary information from a large amount of information. Single-source information systems are often affected by extreme values and outliers, so multi-source information systems are more common and data more reasonable, information fusion is a common method to deal with multi-source information system. Compared with single-valued data, interval-valued data can describe the uncertainty and random change of data more effectively. This article proposes a novel interval-valued multi-source information fusion method: A multi-source information fusion method based on dependency interval. This method needs to construct a dependency function, which takes into account the interval length and the number of data points in the interval, so as to make the obtained data more centralized and eliminate the influence of outliers and extreme values. Due to the unfixed boundary of the dependency interval, a median point within the interval is selected as a bridge to simplify the acquisition of the dependency interval. Furthermore, a multi-source information system fusion algorithm based on dependency intervals was proposed, and experiments were conducted on 9 UCI datasets to compare the classification accuracy and quality of the proposed algorithm with traditional information fusion methods. The experimental results show that this method is more effective than the maximum interval method, quartile interval method, and mean interval method, and the validity of the data has been proven through hypothesis testing.

关键词： Interval-valued information fusion multi-source information system dependency interval

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A Novel Dynamic Fusion Approach Using information Entropy for Interval-Valued Ordered Datasets

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IEEE TRANSACTIONS ON BIG DATA 2023年第3期9卷 845-859页

作者： Xu, Weihua Pan, Yanzhou Chen, Xiuwei Ding, Weiping Qian, Yuhua Southwest Univ Coll Artificial Intelligence Chongqing 400715 Peoples R China Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China Shanxi Univ Inst Big Data Sci & Ind Taiyuan 030006 Shanxi Peoples R China

information fusion is capable of fusing and transforming information originated from multiple sources into an integrated representation. As an important representative of information, interval-valued ordered data aims at characterizing inaccurate and ambiguous information. However, existing methods are inappropriate when applied to the multi-source fusion for them. In addition, it is inevitable that in daily-life the sources and attributes of multi-source information systems may update at the same time. Hence there is a need to fuse data as efficiently as possible. Inspired by these deficiencies, we pay attention to the effective and efficient fusion of multi-source interval-valued ordered data in varieties of cases. First, the concepts of fuzzy dominance relation and dominance classes based on it are put forward between any two samples of interval-valued ordered information systems. Second, we define the fuzzy dominating and dominated conditional entropy. Then the fusion model is established and it is multi-source interval-valued ordered data oriented. Furthermore, it is true that there are numerous real-life applications related to concurrent change of both sources and attributes. Consequently, we design four incremental mechanisms and algorithms for fusing multi-source interval-valued data on the ground of the static condition. Eventually, a series of experiments is carried out on twelve datasets to verify that the proposed fusion approach outperforms other comparative methods on efficacy. Meanwhile, our incremental fusion algorithms are efficient compared with the static one for updating multi-source interval-valued data when sources and attributes are in the simultaneous variation.

关键词： Entropy Big Data information systems Rough sets information entropy Sensor phenomena and characterization Fuses Dynamic fusion information entropy interval-valued ordered data multi-source information system

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Matrix-based multi-granulation fusion approach for dynamic updating of knowledge in multi-source information

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KNOWLEDGE-BASED systemS 2023年第1期262卷

作者： Zhang, Xiaoyan Huang, Xudong Xu, Weihua Southwest Univ Coll Artificial Intelligence Chongqing 400715 Peoples R China

multisource information fusion is an important big data technology that plays a crucial role in the fields of data mining and knowledge discovery. multigranulation information fusion is an effective technique for obtaining critical information from multisource data. However, obtaining meaningful information from a dynamic multisource information system using a multigranulation fusion strategy has rarely been researched. Therefore, this study explores a matrix-based dynamic updating strategy for multigranulation fusion operators. First, a matrix-based method for computing multigranular fusion operators is proposed. Second, the matrix-based dynamic updating mechanism of the multigranularity fusion operator is discussed and constructed for four cases (deleting objects, adding objects, deleting sources, and adding sources). Finally, four groups of dynamic algorithms and static methods are compared to verify the effectiveness of the dynamic algorithm. The experimental findings show that the matrix-dynamic updating multigranulation fusion operator approach is effective and efficient. (c) 2023 Elsevier B.V. All rights reserved.

关键词： Fixed aggregation operator Matrix multi-source information system Possible aggregation operator

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A novel approach to information fusion in multi-source datasets: A granular computing viewpoint

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information SCIENCES 2017年 378卷 410-423页

作者： Xu, Weihua Yu, Jianhang Chongqing Univ Technol Sch Math & Stat Chongqing 400054 Peoples R China

The advent of Big Data has seen both the sources and volumes of data increase rapidly. A multi-source information system can be used to represent information drawn from multiple sources. However, some of these sources are of less importance than others, and some are essentially worthless. Selecting the most valuable sources and efficiently fusing information are therefore core issues in the field of data science. To investigate this matter, we first propose internal-confidence and external-confidence degrees to estimate the reliability of each information source within a multi-source information system. A source selection principle is then constructed, allowing worthy and reliable information sources to be chosen. Furthermore, a new information fusion method is constructed by transforming the original information of each object into a triangular fuzzy information granule, and some uncertainty measures of this fusion process are studied. Finally, to interpret and comprehend the proposed theories and algorithm, extensive experiments are performed on six datasets to verify that our approach can deal with practical issues. The results indicate that the proposed triangular fuzzy granule fusion approach is efficient and effective for information fusion in multi-source datasets. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Granular computing information fusion multi-source information system Triangular fuzzy granule

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Evidence theory fusion method based on variable granulation rough set 8

Evidence theory fusion method based on variable granulation ...

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8th International Conference on information Technology in Medicine and Education (ITME)

作者： Liu, Peiyu Pan, Xiao Ren, Min Lv, Lin Shandong Normal Univ Sch Informat Sci & Engn Shandong Prov Key Lab Distributed Comp Software N Jinan 250358 Shandong Peoples R China Shandong Univ Finance & Econ Sch Math & Quant Econ Jinan 250014 Shandong Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Shandong Peoples R China

ISBN: (纸本)9781509039067

Variable granulation rough set and D-S evidence theory are two kinds of complementary evidence fusion method. In this paper, we discuss the two theories in detail, and proposing an evidence fusion method based on variable granulation rough set according to the contact between the two theories. This method transforms the multi-source evidence fusion problem to the data analysis problem of multi-source information system. We use granulation space to represent evidence space, and use granulation distance to represent evidence distance, so that we can judge if there is a conflict between the evidences or not. And then the corresponding fusion rules are used to set the parameter beta and carry on the evidence fusion respectively. At the end of the paper, an example of calculation shows that the fusion method is effectively.

关键词： Granular computing Variable granulation rough set multi-source information system D-S evidence theory Approximate space Evidence fusion

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A category approach to relation preserving functions in rough set theory

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2015年第PartA期56卷 71-86页

作者： Diker, Murat Hacettepe Univ Dept Math TR-06532 Ankara Turkey

The category Rel whose objects are all pairs (U, r), where r is a relation on a universe U, and whose morphisms are relation-preserving mappings is a canonical example in category theory. One of the convenient categories for rough set systems on a single universe is Rel since the objects of Rel are approximation spaces. The morphisms of a ground category dfTex whose objects are textures can be characterized by definability. Therefore, we particularly investigate a textural counterpart of the category Rel denoted by diRel of textural approximation spaces and direlation preserving difunctions. In this respect, we prove that diRel is a topological category over dfTex and Rel is a full subcategory of diRel. In view of the textural arguments, we show that the preimage of a definable subset of an approximation space with respect to a relation preserving function is also definable in the category Rere of reflexive relations. Furthermore, we denote the category of all information system homomorphisms and all information systems by IS and we show that the category ISO of all information system homomorphisms and all object-irreducible information systems where the attribute functions are surjective is embeddable into Rel. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Approximation operator Definability Direlation multi-source information system Relation preserving function Rough set Texture space

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