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

Jun Cao, Fanyu Wang (), Zhenping Xie () and She Song ()
Additional contact information
Jun Cao: Jiangnan University, College of Artificial Intelligence and Computer Science
Fanyu Wang: Jiangnan University, College of Artificial Intelligence and Computer Science
Zhenping Xie: Jiangnan University, College of Artificial Intelligence and Computer Science
She Song: Inspur Zhuoshu Big Data Industry Development Company Limited

A chapter in Proceedings of the 2023 2nd International Conference on Urban Planning and Regional Economy (UPRE 2023), 2023, pp 55-70 from Springer

Abstract: 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.

Keywords: missing data filling; random forest; ensemble learning; multi-view learning (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-218-7_8

Ordering information: This item can be ordered from
http://www.springer.com/9789464632187

DOI: 10.2991/978-94-6463-218-7_8

Access Statistics for this chapter

More chapters in Advances in Economics, Business and Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().