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  • Economics  (3)
  • 1
    Online Resource
    Online Resource
    Informa UK Limited ; 2014
    In:  Journal of the American Statistical Association Vol. 109, No. 506 ( 2014-04-03), p. 635-646
    In: Journal of the American Statistical Association, Informa UK Limited, Vol. 109, No. 506 ( 2014-04-03), p. 635-646
    Type of Medium: Online Resource
    ISSN: 0162-1459 , 1537-274X
    RVK:
    RVK:
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2014
    detail.hit.zdb_id: 2064981-2
    detail.hit.zdb_id: 207602-0
    Location Call Number Limitation Availability
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  • 2
    Online Resource
    Online Resource
    Hindawi Limited ; 2022
    In:  Mobile Information Systems Vol. 2022 ( 2022-4-26), p. 1-14
    In: Mobile Information Systems, Hindawi Limited, Vol. 2022 ( 2022-4-26), p. 1-14
    Abstract: Detecting potential defaults or bad debt with limited information has become a huge challenge. The main difficulties faced by the credit scoring are sample imbalance and poor classification performance. For this reason, we first proposed the auxiliary conditional tabular generative adversarial network (ACTGAN) to generate sufficient default transaction samples from the original data, then we designed a model based on ResNet-LSTM used for feature extraction, which includes two submodels of ResNet and LSTM to extract static local features and dynamic temporal features from the original data, respectively. After that, a spatiotemporal attention module is added to calculate the importance of the two submodel’s output in order to extract more critical information. Finally, we applied the focus loss function into the XGBoost classifier to improve the probability output of the credit default risk. We verified the designed credit scoring model in two real-world datasets. The experimental results showed that ACTGAN can effectively solve the problem of data imbalance. The ResNet-LSTM+XGBoost model for classification is better than other traditional algorithms in F1 value, AUC, and KS value, which proves the effectiveness and portability of this model in the field of credit scoring.
    Type of Medium: Online Resource
    ISSN: 1875-905X , 1574-017X
    RVK:
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2187808-0
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  • 3
    Online Resource
    Online Resource
    Hindawi Limited ; 2016
    In:  Mobile Information Systems Vol. 2016 ( 2016), p. 1-11
    In: Mobile Information Systems, Hindawi Limited, Vol. 2016 ( 2016), p. 1-11
    Abstract: As tremendous mobile devices access to the Internet in the future, the cells which can provide high data rate and more capacity are expected to be deployed. Specifically, in the next generation of mobile communication 5G, cloud computing is supposed to be applied to radio access network. In cloud radio access network (Cloud-RAN), the traditional base station is divided into two parts, that is, remote radio heads (RRHs) and base band units (BBUs). RRHs are geographically distributed and densely deployed, so as to achieve high data rate and low latency. However, the ultradense deployment inevitably deteriorates spectrum efficiency due to the severer intercell interference among RRHs. In this paper, the downlink spectrum efficiency can be improved through the cooperative transmission based on forming the coalitions of RRHs. We formulate the problem as a coalition formation game in partition form. In the process of coalition formation, each RRH can join or leave one coalition to maximize its own individual utility while taking into account the coalition utility at the same time. Moreover, the convergence and stability of the resulting coalition structure are studied. The numeric simulation result demonstrates that the proposed approach based on coalition formation game is superior to the noncooperative method in terms of the aggregate coalition utility.
    Type of Medium: Online Resource
    ISSN: 1574-017X , 1875-905X
    RVK:
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2016
    detail.hit.zdb_id: 2187808-0
    Location Call Number Limitation Availability
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