A multi-level classification based ensemble and feature extractor for credit risk assessment.

Yuanyuan Wang, Zhuang Wu, Jing Gao, Chenjun Liu, Fangfang Guo

Open access · goldAbstract read

In one paragraph

Article in PeerJ. Computer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

3.8field-weighted citation impact, top 7% of its field

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Yuanyuan WangSchool of Management and Engineering, Capital University of Economics and Business, BeiJing, Fengtai District, Beijing, China.ORCID 0000-0001-9051-7334

Zhuang WuSchool of Management and Engineering, Capital University of Economics and Business, BeiJing, Fengtai District, Beijing, China.

Jing GaoSchool of Management and Engineering, Capital University of Economics and Business, BeiJing, Fengtai District, Beijing, China.

Chenjun LiuSchool of Management and Engineering, Capital University of Economics and Business, BeiJing, Fengtai District, Beijing, China.

Fangfang GuoSchool of Management and Engineering, Capital University of Economics and Business, BeiJing, Fengtai District, Beijing, China.

Capital University of Economics and Business · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the growth of people's demand for loans, banks and other financial institutions put forward higher requirements for customer credit risk level classification, the purpose is to make better loan decisions and loan amount allocation and reduce the pre-loan risk. This article proposes a Multi-Level Classification based Ensemble and Feature Extractor (MLCEFE) that incorporates the strengths of sampling, feature extraction, and ensemble classification. MLCEFE uses SMOTE + Tomek links to solve the problem of data imbalance and then uses a deep neural network (DNN), auto-encoder (AE), and principal component analysis (PCA) to transform the original variables into higher-level abstract features for feature extraction. Finally, it combined multiple ensemble learners to improve the effect of personal credit risk multi-classification. During performance evaluation, MLCEFE has shown remarkable results in the multi-classification of personal credit risk compared with other classification methods.

Indexed as

Ensemble learningMulti-level classificationPersonal credit riskSMOTE + Tomek links sampling

Identifiers

PMID38435611

DOI10.7717/peerj-cs.1915

PMCPMC10909241

OpenAlexW4392341833

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

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.