Expert finding by the Dempster‐Shafer theory for evidence combination

Mahani, Nafiseh Torkzadeh; Dehghani, Mostafa; Mirian, Maryam S.; Shakery, Azadeh

doi:10.1111/exsy.12231

Cited by 10 publications

(6 citation statements)

References 32 publications

(48 reference statements)

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“…Dempster-Shafer (DS) is a mathematical theory for proof based on belief functions and plausible reasoning, which is used to combine separate pieces of information (evidence) to calculate the probability of an event [8][9]. The stages in the Dempster-Shafer method are [10][11]:…”

Section: Dempster-shafer Methodsmentioning

confidence: 99%

Comparative Analysis of Dempster-Shafer Method and Certainty Factor Method On Personality Disorders Expert Systems

Yuwono

Fadlil

Sunardi

2019

SJI

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Kepribadian adalah sifat dan kebiasaan seseorang yang membedakannya dari orang lain. Identifikasi gangguan kepribadian penting untuk semua orang. Kondisi kepribadian yang mencerminkan kondisi mental yang sehat. Namun, tidak bagi manusia untuk tidak bisa menerima tekanan pada diri sendiri. Dalam beberapa kasus, banyak orang ingin melakukan pengujian dengan sumber daya yang cukup dan verifikasi yang sesuai. Untuk mengatasinya, diperlukan tes validasi untuk metode deteksi cacat. Metode yang mendukung validasi adalah Metode Dempster-Shafer dengan Metode Faktor Kepastian.Analisis validasi dilakukan pada metode Dempster-Shafer dengan metode Certainty Factor karena menimbang nilai kerusakan kedua metode ini diperlukan dari data hipotesis atau perubahan dalam bentuk data simptom. Analisis validasi dilakukan pada nilai bobot yang dihasilkan untuk setiap metode yang diterapkan. Dari hasil analisis validasi, nilai bobot yang diperoleh dengan metode Demp-ster-Shafer dapat menghasilkan nilai bobot yang lebih akurat.

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Section: Dempster-shafer Methodsmentioning

confidence: 99%

Comparative Analysis of Dempster-Shafer Method and Certainty Factor Method On Personality Disorders Expert Systems

Yuwono

Fadlil

Sunardi

2019

SJI

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“…Their experimental results showed the differences in user behaviour of active users and actual experts, in terms of knowledge generation, community participation, and temporal change. Torkzadeh Mahani, Dehghani, Mirian, Shakery, and Taheri (2018) employed the Dempster–Shafer theory to combine the different information sources for the determination of experts. Faisal et al (2019) made the first attempt to apply bibliometric technique for expertise ranking problem in CQA.…”

Section: Related Workmentioning

confidence: 99%

Domain problem‐solving expert identification in community question answering

Tang

et al. 2020

Expert Systems

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Question-Answering (Q&A) services provide internet users with platforms to exchange knowledge and ideas. The development of Q&A sites, or Community Question Answering (CQA), mainly depends on the high-quality content continuously contributed by users with high-level expertise, who can be recognized as experts. Expert finding is an important task for the authorities of Q&A communities to encourage commitment. In a highly competitive market environment, CQA managers have to take measures to retain and nurture users, especially superior contributors. However, current expertise scoring techniques adopted in CQA often give much credit to very active users and fail to identify real experts. This study aims to develop a robust and practical expert identification framework for Q&A communities, by combining welldesigned expertise scoring technique and probabilistic clustering model. With regard to expert identification, a numerical metric of users' expertise is developed as the optimal expert finding strategy, and a clustering algorithm based on Gaussian-Gamma mixture model (GGMM) is proposed to efficiently distinguish experts from nonexperts. In the experiments, the proposed method is applied to real-world datasets collected from subcommunities of Stack Exchange Q&A networks. Results obtained from comparative experiments show that our method achieves better performance than the state-of-the-art methods and demonstrate the effectiveness of the proposed framework. The analysis shows that the framework which combines the proposed expertise scoring technique and Gaussian-Gamma mixture clustering model is capable of detecting excellent domain problem-solving experts who exhibit both domain interest and expertise.

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“…The problem of combining several pieces of information is addressed by Dempster-Shafer Theory of evidence (DST) (Shafer, 1976) and is suitable to consider the limited information when it has both epistemic and aleatory uncertainty (Sentz & Ferson, 2002), even when expert opinion is involved (Torkzadeh-Mahani et al, 2018). This is the case that is assessed in this paper, which deals with information from different sources, at different stages of the project (pieces of information).…”

Section: Combination Of Random Setsmentioning

confidence: 99%

Updating the probability of failure of rock wedges

Hernández-Carrillo

Beltrán

2019

Earth sci. res. j.

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In hard rock masses, discontinuities control the slope stability, rather than block matrix breakage. The relative position of joints and slope face defines the most likely mechanisms of failure. Among these mechanisms, the wedge failure is one of the most common ways of failure in which joint sets dip and dip direction, slope geometry and direction, external forces (including water pressure and earthquake) and rock and joints mechanical properties control the stability. The determination of these input parameters is not straightforward, mainly due to their variability and the limited amount of information available. Besides, in most projects, input parameters come from different sources (e.g., expert opinion, back-calculation, laboratory tests, field test or different project stages). Therefore, this limited information from different sources should be appropriately incorporated into the stability analysis to assist the design and decision-making process. In this context, random sets arise as a powerful tool to combine different sources of information and to perform a reliability assessment under limited information. This feature makes it possible to update the probability of failure as new evidence is available. With this framework, this paper presents a reliability assessment of wedge stability in a rock slope of a sandstone quarry, located in Une Cundinamarca, where information on mechanical and geometrical parameters has been collected for 20 years.

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Expert finding by the Dempster‐Shafer theory for evidence combination

Cited by 10 publications

References 32 publications

Comparative Analysis of Dempster-Shafer Method and Certainty Factor Method On Personality Disorders Expert Systems

Comparative Analysis of Dempster-Shafer Method and Certainty Factor Method On Personality Disorders Expert Systems

Domain problem‐solving expert identification in community question answering

Updating the probability of failure of rock wedges

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