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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 9
2667-2672

Credit evaluation method for high dimensional missing unbalanced data

Fan Dongxing
Ye Chunming
School of Management, University of Shanghai for Science & Technology, Shanghai 200093, China
DOI: 10.19734/j.issn.1001-3695.2021.01.0007

Abstract

Traditional random forest filling method does not consider the problem of high-dimensional imbalance, which leads to filling untargeted. In addition, it need to prefill missing with 0, which may introduce noise and lead to the decrease in prediction accuracy. Therefore, this paper proposed a missing value filling method based on Q-learning and random forest(QL-RF). After feature selection, this method used Q-learning to weigh the filling accuracy and the filling quantity, then it selected the valuable samples and feature combinations by calculating reward, and it used the redundant features to fill the missing of important features with the minority samples filled mainly. Moreover, in order to improve the classification effect of imbalanced data, it proposed an ensemble classification model(QXB) based on bagging framework, which integrated quantum particle swarm optimization(QPSO) and XGBoost. The experiment results show that QL-RF is superior to traditional RF filling method in terms of G-means, F1-measure and AUC, and QXB is significantly superior to SMOTE-RF and SMOTE-XGBoost. The proposed methods can effectively deal with the missing and classification problems under high-dimensional imbalance data.

Foundation Support

国家自然科学基金资助项目(71840003)
上海市科委“科技创新行动计划”软科学重点项目(20692104300)
上海理工大学科技发展资助项目(2018KJFZ043)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.01.0007
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 9
Section: Algorithm Research & Explore
Pages: 2667-2672
Serial Number: 1001-3695(2021)09-019-2667-06

Publish History

[2021-09-05] Printed Article

Cite This Article

樊东醒, 叶春明. 一种面向高维缺失不平衡数据的信用评估方法 [J]. 计算机应用研究, 2021, 38 (9): 2667-2672. (Fan Dongxing, Ye Chunming. Credit evaluation method for high dimensional missing unbalanced data [J]. Application Research of Computers, 2021, 38 (9): 2667-2672. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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