4.6 Article

A novel deep ensemble model for imbalanced credit scoring in internet finance

Related references

Note: Only part of the references are listed.
Article Computer Science, Artificial Intelligence

Deep reinforcement learning with the confusion-matrix-based dynamic reward function for customer credit scoring

Yadong Wang et al.

Summary: The study introduces a deep Q-network model with a confusion-matrix-based dynamic reward function, which is proven to effectively improve customer credit scoring performance, accelerate model convergence, and enhance stability.

EXPERT SYSTEMS WITH APPLICATIONS (2022)

Article Business

Combining feature selection, instance selection, and ensemble classification techniques for improved financial distress prediction

Chih-Fong Tsai et al.

Summary: Studies have shown that bankruptcy prediction and credit scoring can be improved through data preprocessing and classifier ensembles. This study focused on the combination of three factors and found that better prediction models can be developed by carefully considering their interactions.

JOURNAL OF BUSINESS RESEARCH (2021)

Article Computer Science, Artificial Intelligence

A new deep learning ensemble credit risk evaluation model with an improved synthetic minority oversampling technique

Feng Shen et al.

Summary: Recent research has shown that deep learning outperforms traditional machine learning methods in credit risk evaluation, and classifier ensembles are more effective than single classifiers. A new deep learning ensemble credit risk evaluation model was developed to address imbalanced credit data, demonstrating better performance in experimental tests.

APPLIED SOFT COMPUTING (2021)

Article Computer Science, Artificial Intelligence

Conditional Wasserstein GAN-based oversampling of tabular data for imbalanced learning

Justin Engelmann et al.

Summary: This paper explores the potential of using Generative Adversarial Networks (GANs) for oversampling, focusing on a conditional Wasserstein GAN approach for modeling tabular datasets with numerical and categorical variables. The study shows that GAN-based oversampling is competitive in the context of credit scoring compared to standard oversampling methods, suggesting that GAN architectures for tabular data are valuable tools for data scientists.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

Article Computer Science, Information Systems

Impact of resampling methods and classification models on the imbalanced credit scoring problems

Jin Xiao et al.

Summary: This study proposed a new benchmark models comparison framework for imbalanced credit scoring, experimentally comparing the performance of various resampling methods and classification models, and analyzing the optimal combinations of them.

INFORMATION SCIENCES (2021)

Article Computer Science, Artificial Intelligence

Cost-sensitive semi-supervised selective ensemble model for customer credit scoring

Jin Xiao et al.

KNOWLEDGE-BASED SYSTEMS (2020)

Article Automation & Control Systems

A Hybrid Classification Framework Based on Clustering

Jin Xiao et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

Article Computer Science, Artificial Intelligence

Circular Complex-Valued GMDH-Type Neural Network for Real-Valued Classification Problems

Jin Xiao et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2020)

Article Computer Science, Artificial Intelligence

Cost-sensitive deep forest for price prediction

Chao Ma et al.

PATTERN RECOGNITION (2020)

Article Biochemical Research Methods

MinE-RFE: determine the optimal subset from RFE by minimizing the subset-accuracy-defined energy

Ran Su et al.

BRIEFINGS IN BIOINFORMATICS (2020)

Article Computer Science, Information Systems

Using generative adversarial networks for improving classification effectiveness in credit card fraud detection

Ugo Fiore et al.

INFORMATION SCIENCES (2019)

Article Computer Science, Artificial Intelligence

A Deep Learning Approach to Competing Risks Representation in Peer-to-Peer Lending

Fei Tan et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2019)

Article Multidisciplinary Sciences

Deep forest

Zhi-Hua Zhou et al.

NATIONAL SCIENCE REVIEW (2019)

Article Computer Science, Information Systems

A Deep Learning Approach for Credit Scoring of Peer-to-Peer Lending Using Attention Mechanism LSTM

Chongren Wang et al.

IEEE ACCESS (2019)

Article Computer Science, Artificial Intelligence

Fuzzy-Rough Instance Selection Combined with Effective Classifiers in Credit Scoring

ZhanFeng Liu et al.

NEURAL PROCESSING LETTERS (2018)

Article Computer Science, Artificial Intelligence

Effective data generation for imbalanced learning using conditional generative adversarial networks

Georgios Douzas et al.

EXPERT SYSTEMS WITH APPLICATIONS (2018)

Article Computer Science, Information Systems

Heterogeneous Ensemble for Default Prediction of Peer-to-Peer Lending in China

Wei Li et al.

IEEE ACCESS (2018)

Article Computer Science, Artificial Intelligence

GMDH-based semi-supervised feature selection for customer classification

Jin Xiao et al.

KNOWLEDGE-BASED SYSTEMS (2017)

Article Economics

The information value of online social networks: Lessons from peer-to-peer lending

Seth Freedman et al.

INTERNATIONAL JOURNAL OF INDUSTRIAL ORGANIZATION (2017)

Article Engineering, Industrial

A novel multistage deep belief network based extreme learning machine ensemble learning paradigm for credit risk assessment

Lean Yu et al.

FLEXIBLE SERVICES AND MANUFACTURING JOURNAL (2016)

Article Management

Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research

Stefan Lessmann et al.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2015)

Article Computer Science, Artificial Intelligence

Coupling different methods for overcoming the class imbalance problem

Loris Nanni et al.

NEUROCOMPUTING (2015)

Article Computer Science, Artificial Intelligence

Improving class probability estimates for imbalanced data

Byron C. Wallace et al.

KNOWLEDGE AND INFORMATION SYSTEMS (2014)

Article Management

On the suitability of resampling techniques for the class imbalance problem in credit scoring

A. I. Marques et al.

JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY (2013)

Article Computer Science, Artificial Intelligence

On the use of data filtering techniques for credit risk prediction with instance-based models

V. Garcia et al.

EXPERT SYSTEMS WITH APPLICATIONS (2012)

Article Economics

Instance sampling in credit scoring: An empirical study of sample size and balancing

Sven F. Crone et al.

INTERNATIONAL JOURNAL OF FORECASTING (2012)

Article Biochemical Research Methods

Permutation importance: a corrected feature importance measure

Andre Altmann et al.

BIOINFORMATICS (2010)

Article Computer Science, Artificial Intelligence

Gene selection for cancer classification using support vector machines

I Guyon et al.

MACHINE LEARNING (2002)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)