CATE: Contrastive augmentation and tree-enhanced embedding for credit scoring-Reference-Cited by-同舟云学术

CATE: Contrastive augmentation and tree-enhanced embedding for credit scoring

Published:2023-12 Issue: Volume:651 Page:119447
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Gao Ying,Xiao Haolang^ORCID,Zhan Choujun,Liang Lingrui,Cai Wentian,Hu Xiping

Funder

National Natural Science Foundation of China

Guangzhou Municipal Science and Technology Program key projects

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference47 articles.

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2. Credit scoring using three-way decisions with probabilistic rough sets;Maldonado;Inf. Sci.,2020

3. Imbalanced enterprise credit evaluation with DTE-SBD: decision tree ensemble based on SMOTE and bagging with differentiated sampling rates;Sun;Inf. Sci.,2018

4. Cost-sensitive learning for profit-driven credit scoring;Petrides;J. Oper. Res. Soc.,2022

5. A combined entropy-based approach for a proactive credit scoring;Carta;Eng. Appl. Artif. Intell.,2020

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1. How Can Credit Scoring Benefit from Machine Learning? SWOT Analysis;Lecture Notes in Networks and Systems;2024

2. Classification of Borrowers Using an Immune Model of Artificial Intelligence in Credit Scoring Tasks;2023 IEEE XVI International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering (APEIE);2023-11-10