A novel method for credit scoring based on feature transformation and ensemble model.

Hongxiang Li, Ao Feng, Bin Lin, Houcheng Su, Zixi Liu, Xuliang Duan, Haibo Pu, Yifei Wang

Abstract read

In one paragraph

Article in PeerJ. Computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

A multi-level classification based ensemble and feature extractor for credit risk assessment.PeerJ. Computer science · 2024
Article
Using machine learning-based binary classifiers for predicting organizational members' user satisfaction with collaboration software.PeerJ. Computer science · 2023
Article

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5 · Who and what money

Authors and funding

8 authors.

Hongxiang Li *College of Information Engineering, Sichuan Agricultural University, Ya'an, Sichuan, China.

Ao Feng *College of Information Engineering, Sichuan Agricultural University, Ya'an, Sichuan, China.

Bin LinCollege of Information Engineering, Sichuan Agricultural University, Ya'an, Sichuan, China.

Houcheng SuCollege of Information Engineering, Sichuan Agricultural University, Ya'an, Sichuan, China.

Zixi LiuCollege of Information Engineering, Sichuan Agricultural University, Ya'an, Sichuan, China.

Xuliang DuanCollege of Information Engineering, Sichuan Agricultural University, Ya'an, Sichuan, China.

Haibo PuCollege of Information Engineering, Sichuan Agricultural University, Ya'an, Sichuan, China.

Yifei WangResearch Institute of Economics and Management, Southwest University of Finance & Economics, Chengdu, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Credit scoring is a very critical task for banks and other financial institutions, and it has become an important evaluation metric to distinguish potential defaulting users. In this paper, we propose a credit score prediction method based on feature transformation and ensemble model, which is essentially a cascade approach. The feature transformation process consisting of boosting trees (BT) and auto-encoders (AE) is employed to replace manual feature engineering and to solve the data imbalance problem. For the classification process, this paper designs a heterogeneous ensemble model by weighting the factorization machine (FM) and deep neural networks (DNN), which can efficiently extract low-order intersections and high-order intersections. Comprehensive experiments were conducted on two standard datasets and the results demonstrate that the proposed approach outperforms existing credit scoring models in accuracy.

Indexed as

AutoEncoderBoosting treeCredit scoringDeep neural networkFactorization machineFeature transformation

Identifiers

PMID34151000

DOI10.7717/peerj-cs.579

PMCPMC8189024

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.