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Mixed Credit Scoring Model of Logistic Regression and Evidence Weight in the Background of Big Data

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Book cover Intelligent Systems Design and Applications (ISDA 2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 940))

Abstract

Aims of this paper is to amalgamate the logistic regression algorithm under big data with the weight of evidence. To construct a new credit scoring model to analyze the user’s credit score and then divide the user into trustworthy customers and non-trustworthy customers respectively. Calculation of credit score shows the relationship between independent and dependent variable. The weight of the evidence is calculated by the maximum correlation orthogonal transform, which can have a significant effect on models with higher correlation. Due to the error in the data collected, the logistic regression error is large. Therefore, it is suggested that by constructing a hybrid scoring model a more accurate credit score can be obtained. This helps to improve the prediction accuracy of the credit score.

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Author information

Authors and Affiliations

  1. School of Business Administration, Southwestern University of Finance and Economics, Chengdu, 611130, China

    Keqin Chen

  2. Department of Information and Software Engineering, Chengdu Neusoft University, Dujianyan, Chengdu, 611844, Sichuan, China

    Keqin Chen, Kun Zhu, Yixin Meng & Amit Yadav

  3. Crescent Institute of Science and Technology, Vandalur, Chennai, 600048, India

    Asif Khan

Authors
  1. Keqin Chen
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  2. Kun Zhu
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  3. Yixin Meng
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  4. Amit Yadav
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  5. Asif Khan
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Corresponding author

Correspondence to Keqin Chen .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Aswani Kumar Cherukuri

  3. Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  4. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

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Chen, K., Zhu, K., Meng, Y., Yadav, A., Khan, A. (2020). Mixed Credit Scoring Model of Logistic Regression and Evidence Weight in the Background of Big Data. In: Abraham, A., Cherukuri, A.K., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-16657-1_40

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