[1]国强强,朱振方.基于 LightGBM 算法的移动用户信用评分研究[J].计算机技术与发展,2020,30(09):210-215.[doi:10. 3969 / j. issn. 1673-629X. 2020. 09. 038]
　GUO Qiang-qiang,ZHU Zhen-fang.Research on Mobile User Credit Score Based on LightGBM Algorithm[J].,2020,30(09):210-215.[doi:10. 3969 / j. issn. 1673-629X. 2020. 09. 038]
点击复制

基于 LightGBM 算法的移动用户信用评分研究()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年09期
页码:
210-215
栏目:
应用开发研究
出版日期:
2020-09-10

文章信息/Info

Title:
Research on Mobile User Credit Score Based on LightGBM Algorithm
文章编号:
1673-629X(2020)09-0210-06
作者:
国强强; 朱振方
山东交通学院 信息科学与电气工程学院,山东 济南 250357
Author(s):
GUO Qiang-qiang; ZHU Zhen-fang
Department of Information Science and Electrical Engineering,Shandong Jiaotong University,Jinan 250357,China
关键词:
评分预测; LightGBM 算法; K-means 算法; 特征数据; 线性相关性; 随机森林; 信用评分
Keywords:
score prediction; LightGBM algorithm; K-means algorithm; data features; linear dependence; random forest; credit scoring
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 09. 038
摘要:
随着科技进步、社会的发展,个人信用分值对于个人愈加重要,而传统的信用评分主要以个人消费能力等少数的维度来衡量,难以全面、客观、及时地反映个人的信用。 旨在解决面向大样本、高维度数据的环境下的信用分预测问题,提出一种基于 LightGBM 算法的移动用户信用评分算法,完善信用评分体系。 首先分析线性相关性来构建特征集合,然后通过 K-means 算法对特征集合进行聚类分析,最后通过 LightGBM 模型构建信用评分模型。 通过在数字中国创新大赛所提供的真实数据上的实验表明,该方法能够充分挖掘数据特征并且精准地预测用户信用评分,较 GBDT、XGBoost 等算法具有较高的准确率和计算效率。 通过对线性相关性分析基础上的数据特征集合进行聚类分析,并将其应用到基于 LightGBM 信用评分模型,能够更加准确地预测移动用户信用评分。
Abstract:
With the progress of science and technology and the development of society,personal credit score is becoming more and more important to indivi-duals. However,the traditional credit score is mainly measured by a few dimensions such as personal consumption ability,which is difficult to reflect personal credit comprehensively,objectively and timely. In order to address the problem of credit score prediction in the environment of large sample and high - dimensional data,we propose a mobile user credit score algorithm based on LightGBM algorithm to improve the credit scoring system. The linear correlation is firstly analyzed to construct feature sets,and then the K-means algorithm is used to analyze the clustering of feature sets. Finally,the credit scoring model is built by LightGBM model. Experiments on real data provided by the digital China innovation competition shows that the proposed method can fully mine data features and accurately predict user credit score,which is more accurate and efficient 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爷 credit scores can be predicted more accurately.

相似文献/References:

[1]蒋宗礼,王威,陆晨. 基于均值预估的协同过滤推荐算法改进[J].计算机技术与发展,2017,27(05):1.
　JIANG Zong-li,WANG Wei,LU Chen. 基于均值预估的协同过滤推荐算法改进[J].,2017,27(09):1.
[2]刘金梅,舒远仲,张尚田,等.融合巴氏系数的加权 Slope One 算法[J].计算机技术与发展,2020,30(11):74.[doi:10. 3969 / j. issn. 1673-629X. 2020. 11. 014]
　LIU Jin-mei,SHU Yuan-zhong,ZHANG Shang-tian,et al.A Weighted Slope One Algorithm Based on Bhattacharyya Coefficient[J].,2020,30(09):74.[doi:10. 3969 / j. issn. 1673-629X. 2020. 11. 014]
[3]刘金梅,舒远仲,张尚田.基于评分填充和时间的加权 Slope One 算法[J].计算机技术与发展,2021,31(01):35.[doi:10. 3969 / j. issn. 1673-629X. 2021. 01. 007]
　LIU Jin-mei,SHU Yuan-zhong,ZHANG Shang-tian.A Weighted Slope One Algorithm Based on Rating Filling and Time[J].,2021,31(09):35.[doi:10. 3969 / j. issn. 1673-629X. 2021. 01. 007]
[4]潘理虎,郝彦杰,周耀辉,等.基于文本卷积的多因素煤炭产品推荐模型[J].计算机技术与发展,2021,31(04):198.[doi:10. 3969 / j. issn. 1673-629X. 2021. 04. 034]
　PAN Li-hu,HAO Yan-jie,ZHOU Yao-hui,et al.Multi Factor Coal Product Recommendation Model Based onText Convolution[J].,2021,31(09):198.[doi:10. 3969 / j. issn. 1673-629X. 2021. 04. 034]
[5]刘雯雯,汪皖燕,程树林.融合项目热门惩罚因子改进协同过滤推荐方法[J].计算机技术与发展,2023,33(03):15.[doi:10. 3969 / j. issn. 1673-629X. 2023. 03. 003]
　LIU Wen-wen,WANG Wan-yan,CHENG Shu-lin.Improved Collaborative Filtering Recommendation Method Integrating Item Popularity Punishment Factor[J].,2023,33(09):15.[doi:10. 3969 / j. issn. 1673-629X. 2023. 03. 003]

更新日期/Last Update: 2020-09-10