Proceedings of the 2023 2nd International Conference on Urban Planning and Regional Economy (UPRE 2023)

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Imputation Algorithm for Multi-view Financial Data Based on Weighted Random Forest

Authors
Jun Cao1, Fanyu Wang1, Zhenping Xie1, *, She Song2
1College of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, Jiangsu, China
2Inspur Zhuoshu Big Data Industry Development Company Limited, Wuxi, 214125, Jiangsu, China
*Corresponding author. Email: xiezp@jiangnan.edu.cn
Corresponding Author
Zhenping Xie
Available Online 16 August 2023.
DOI
10.2991/978-94-6463-218-7_8How to use a DOI?
Keywords
missing data filling; random forest; ensemble learning; multi-view learning
Abstract

With the development of information technology, a large amount of multi-view data continues to emerge in the financial field. The absence of these multi-view data samples limits the research processing of financial data, while the popular single-view filling algorithm cannot handle the problem of missing multi-view data well. To address this problem, this study proposes a new filling method called Weighted Multi-view Random Forest (WMVRF), which innovatively combines feature importance to calculate view weights and enables missing filling of multi-view data by integrating the label prediction results from multiple views random forests. Several filling algorithms such as MissForest, Generative Adversarial Imputation Network, and KNN are compared on one real dataset and four multi-view public datasets (Handwritten, Webkb, 3Sources, BBCSport). The experimental results show that the proposed method reduces the normalized root mean square error (NRMSE) by 1.6% and outperforms the KNN, GAIN, and EM filling algorithms on the financial dataset compared to RF.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 2nd International Conference on Urban Planning and Regional Economy (UPRE 2023)
Series
Advances in Economics, Business and Management Research
Publication Date
16 August 2023
ISBN
10.2991/978-94-6463-218-7_8
ISSN
2352-5428
DOI
10.2991/978-94-6463-218-7_8How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Jun Cao
AU  - Fanyu Wang
AU  - Zhenping Xie
AU  - She Song
PY  - 2023
DA  - 2023/08/16
TI  - Imputation Algorithm for Multi-view Financial Data Based on Weighted Random Forest
BT  - Proceedings of the 2023 2nd International Conference on Urban Planning and Regional Economy (UPRE 2023)
PB  - Atlantis Press
SP  - 55
EP  - 70
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-218-7_8
DO  - 10.2991/978-94-6463-218-7_8
ID  - Cao2023
ER  -