Mobile User Credit Prediction Based on LightGBM

DOI

10.2991/acsr.k.191223.033How to use a DOI?

Keywords

score prediction, LightGBM algorithm, K-means, feature data

Abstract

LightGBM algorithm is used to build an effective credit score prediction model for mobile users and improve the prediction system of personal credit score. Firstly, linear correlation is analyzed to build feature set, then k-means algorithm is used to analyze feature set clustering, and finally, credit scoring model is built by LightGBM. Experiments on real data provided by the digital China innovation competition show that this method has higher accuracy than GBDT, XGBoost and other algorithms. By clustering the data feature set based on linear correlation analysis and applying it to LightGBM credit scoring model, mobile users' credit scores can be predicted more accurately.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Qiangqiang Guo
AU  - Zhenfang Zhu
AU  - Hongli Pei
AU  - Fuyong Xu
AU  - Qiang Lu
AU  - Dianyuan Zhang
AU  - Wenqing Wu
PY  - 2019
DA  - 2019/12/24
TI  - Mobile User Credit Prediction Based on LightGBM
BT  - Proceedings of the 2019 International Conference on Big Data, Electronics and Communication Engineering (BDECE 2019)
PB  - Atlantis Press
SP  - 140
EP  - 144
SN  - 2352-538X
UR  - https://doi.org/10.2991/acsr.k.191223.033
DO  - 10.2991/acsr.k.191223.033
ID  - Guo2019
ER  -