The Experts below are selected from a list of 49659 Experts worldwide ranked by ideXlab platform
Afif Masmoudi - One of the best experts on this subject based on the ideXlab platform.
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credit risk modeling using bayesian network with a latent variable
Expert Systems With Applications, 2019Co-Authors: Khalil Masmoudi, Lobna Abid, Afif MasmoudiAbstract:Abstract Credit risk assessment is an important task for the implementation of the bank policies and commercial strategies. In this paper, we used a discrete Bayesian network with a latent variable to model the payment default of loans subscribers. The proposed Bayesian network includes a built-in Clustering Feature. A full procedure for learning its parameters, based on a customized Expectation-Maximization algorithm was provided. This model allows evaluating the payment default probability taking into account several factors and handling a multi-class situation. Relying on a real data set describing loans contracts, we calibrated the model and performed several analyses. The obtained results highlight a regime switching of the default probability distribution: Two classes were determined showing a change in credit risk profiles.
Xuefeng Chen - One of the best experts on this subject based on the ideXlab platform.
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new Clustering algorithm based fault diagnosis using compensation distance evaluation technique
Mechanical Systems and Signal Processing, 2008Co-Authors: Zhengjia He, Yanyang Zi, Xuefeng ChenAbstract:This paper presents a fault diagnosis method of rotating machinery based on a new Clustering algorithm using a compensation distance evaluation technique (CDET). A two-stage Feature selection and weighting technique is adopted in this algorithm. Feature weights are computed via CDET according to the sensitivity of Features and assigned to the corresponding Features to indicate their different importance in Clustering. Feature weighting highlights the importance of sensitive Features and simultaneously weakens the interference of insensitive Features. The new Clustering algorithm is described and applied to incipient fault and compound fault diagnosis of locomotive roller bearings. The diagnosis result shows the algorithm is able to reliably recognise not only different fault categories and severities but also the compound faults, and demonstrates the superior effectiveness and practicability of the algorithm. Therefore, it is a promising approach to fault diagnosis of rotating machinery.
Khalil Masmoudi - One of the best experts on this subject based on the ideXlab platform.
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credit risk modeling using bayesian network with a latent variable
Expert Systems With Applications, 2019Co-Authors: Khalil Masmoudi, Lobna Abid, Afif MasmoudiAbstract:Abstract Credit risk assessment is an important task for the implementation of the bank policies and commercial strategies. In this paper, we used a discrete Bayesian network with a latent variable to model the payment default of loans subscribers. The proposed Bayesian network includes a built-in Clustering Feature. A full procedure for learning its parameters, based on a customized Expectation-Maximization algorithm was provided. This model allows evaluating the payment default probability taking into account several factors and handling a multi-class situation. Relying on a real data set describing loans contracts, we calibrated the model and performed several analyses. The obtained results highlight a regime switching of the default probability distribution: Two classes were determined showing a change in credit risk profiles.
Zhengjia He - One of the best experts on this subject based on the ideXlab platform.
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new Clustering algorithm based fault diagnosis using compensation distance evaluation technique
Mechanical Systems and Signal Processing, 2008Co-Authors: Zhengjia He, Yanyang Zi, Xuefeng ChenAbstract:This paper presents a fault diagnosis method of rotating machinery based on a new Clustering algorithm using a compensation distance evaluation technique (CDET). A two-stage Feature selection and weighting technique is adopted in this algorithm. Feature weights are computed via CDET according to the sensitivity of Features and assigned to the corresponding Features to indicate their different importance in Clustering. Feature weighting highlights the importance of sensitive Features and simultaneously weakens the interference of insensitive Features. The new Clustering algorithm is described and applied to incipient fault and compound fault diagnosis of locomotive roller bearings. The diagnosis result shows the algorithm is able to reliably recognise not only different fault categories and severities but also the compound faults, and demonstrates the superior effectiveness and practicability of the algorithm. Therefore, it is a promising approach to fault diagnosis of rotating machinery.
Zeyuan Liu - One of the best experts on this subject based on the ideXlab platform.
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an overview of the history of science of science in china based on the use of bibliographic and citation data a new method of analysis based on Clustering with Feature maximization and contrast graphs
Scientometrics, 2020Co-Authors: Jeancharles Lamirel, Yue Chen, Pascal Cuxac, Shadi Al Shehabi, Nicolas Dugue, Zeyuan LiuAbstract:In the first part of this paper, we shall discuss the historical context of Science of Science both in China and at world level. In the second part, we use the unsupervised combination of GNG Clustering with Feature maximization metrics and associated contrast graphs to present an analysis of the contents of selected academic journal papers in Science of Science in China and the construction of an overall map of the research topics’ structure during the last 40 years. Furthermore, we highlight how the topics have evolved through analysis of publication dates and also use author information to clarify the topics’ content. The results obtained have been reviewed and approved by 3 leading experts in this field and interestingly show that Chinese Science of Science has gradually become mature in the last 40 years, evolving from the general nature of the discipline itself to related disciplines and their potential interactions, from qualitative analysis to quantitative and visual analysis, and from general research on the social function of science to its more specific economic function and strategic function studies. Consequently, the proposed novel method can be used without supervision, parameters and help from any external knowledge to obtain very clear and precise insights about the development of a scientific domain. The output of the topic extraction part of the method (Clustering + Feature maximization) is finally compared with the output of the well-known LDA approach by experts in the domain which serves to highlight the very clear superiority of the proposed approach.