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

Xu, Weihua; Yu, Jianhang

doi:10.1016/j.ins.2016.04.009

Cited by 182 publications

(40 citation statements)

References 57 publications

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“…It is anticipated that Granular Computing can lead to new computational paradigms. Throughout the developments in these years, Granular Computing has shown many advantages when dealing with big data, such as attribute reduction [162], multi-source data aggregation [163], and feature selection [164]. We can apply the same theory into network security data collection and analytics.…”

Section: B Future Research Directionsmentioning

confidence: 99%

Security Data Collection and Data Analytics in the Internet: A Survey

Jing

Yan

Pedrycz

2019

IEEE Commun. Surv. Tutorials

101

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Section: B Future Research Directionsmentioning

confidence: 99%

Security Data Collection and Data Analytics in the Internet: A Survey

Jing

Yan

Pedrycz

2019

IEEE Commun. Surv. Tutorials

101

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“…To handle MAGDM information fusion and analysis with IN information effectively, GrC-based approaches own unique superiorities in constructing problem addressing approaches via multi-view problem solving tactics [23,24]. During the past several years, taking full advantage of GrC-based approaches, many scholars and practitioners have obtained fruitful results in merging NSs with rough sets [25][26][27][28][29][30][31][32], formal concept analysis [33,34], three-way decisions [35][36][37], and others [38,39].…”

Section: A Brief Review Of Multigranulation Probabilistic Modelsmentioning

confidence: 99%

Multi-Attribute Group Decision Making Based on Multigranulation Probabilistic Models with Interval-Valued Neutrosophic Information

Zhang

Kang

et al. 2020

Mathematics

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In plenty of realistic situations, multi-attribute group decision-making (MAGDM) is ubiquitous and significant in daily activities of individuals and organizations. Among diverse tools for coping with MAGDM, granular computing-based approaches constitute a series of viable and efficient theories by means of multi-view problem solving strategies. In this paper, in order to handle MAGDM issues with interval-valued neutrosophic (IN) information, we adopt one of the granular computing (GrC)-based approaches, known as multigranulation probabilistic models, to address IN MAGDM problems. More specifically, after revisiting the related fundamental knowledge, three types of IN multigranulation probabilistic models are designed at first. Then, some key properties of the developed theoretical models are explored. Afterwards, a MAGDM algorithm for merger and acquisition target selections (M&A TSs) with IN information is summed up. Finally, a real-life case study together with several detailed discussions is investigated to present the validity of the developed models.

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“…This technique originated in the field of military at the beginning of the eighties and is widely used in different fields with different goals, such as dimensionality reduction, precision and certainty etc [46]. Mathematical tools play an important role in data fusion and many kinds of theories are used by scientists in the information fusion field, such as probability theory [47], neural networks [48][49] and fuzzy subset theory [50][51][52] and evidence theory [53][54].…”

Section: Information Fusionmentioning

confidence: 99%

Borrower-lender Information Fusion for P2P Lending: A Nonparametric Approach

Guo¹,

Jiang²,

Chen³

et al. 2019

ISI

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As known to us, tremendous efforts have been made to exploit information from borrower's credit for loan evaluation in P2P lending, but seldom researches have explored information from investors and borrowers. To this end, we propose an integrated loan evaluation model that exploits and fuses multi-source information from both the borrower and the investor for improving investment decisions in P2P lending. First, based on the borrower's credit, we build a kernel-based credit risk model to quantitatively evaluate each loan. Second, we build an investor composition model that exploits information from the investor's investment behavior for loan evaluation. Then, based on the above two quantitative models and correlation, we define a multi-kernel weight and develop an integrated loan assessment model that can evaluate a loan with both the return and risk. Furthermore, based on the integrated information loan evaluation model, we formalize the investment decisions in P2P lending as a portfolio optimization problem with boundary constraints to help investor make better investment decisions. To validate the proposed model, we perform extensive experiments on the realworld data from the world's largest P2P lending marketplace. Experimental results reveal that the integrated loan evaluation model can significantly enhance investment performance beyond the existing model in P2P market and other baseline models.

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

Cited by 182 publications

References 57 publications

Security Data Collection and Data Analytics in the Internet: A Survey

Security Data Collection and Data Analytics in the Internet: A Survey

Multi-Attribute Group Decision Making Based on Multigranulation Probabilistic Models with Interval-Valued Neutrosophic Information

Borrower-lender Information Fusion for P2P Lending: A Nonparametric Approach

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