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

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

doi:10.7717/peerj-cs.579

Cited by 5 publications

(1 citation statement)

References 40 publications

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“…Ensemble learning methods: Li et al (2021) propose a credit score prediction method that uses an ensemble model and a feature transformation process, including boosting trees and auto-encoders, to solve data imbalance. The results show it outperforms existing models in accuracy.…”

Section: Related Workmentioning

confidence: 99%

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

Wang,

Wu,

Gao

et al. 2024

PeerJ Computer Science

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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.

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Section: Related Workmentioning

confidence: 99%

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

Wang,

Wu,

Gao

et al. 2024

PeerJ Computer Science

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Consumer credit risk assessment: A review from the state-of-the-art classification algorithms, data traits, and learning methods

Zhang,

2024

Expert Systems with Applications

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Adaptive Particle Swarm Optimization Algorithm Ensemble Model Applied to Classification of Unbalanced Data

Zheng

Qin

Liu

2021

Scientific Programming

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Unbalanced data classification is a major challenge in the field of data mining. Random forest, as an ensemble learning method, is usually used to solve the problem of unbalanced data classification. For the existing random forest-based classification prediction model, its hyperparameters are dependent on empirical settings, which leads to the problem of unsatisfactory model performance. In order to make random forest find the optimum modelling corresponding to the character of unbalanced data sets and improve the accuracy of prediction, we apply the improved particle swarm optimization to set reasonable hyperparameters of the model. This paper proposes a random forest-based adaptive particle swarm optimization on data classification, and an adaptive particle swarm used to optimize the hyperparameters in the random forest to ensure that the model can better predict the unbalanced data accurately. Aiming at the premature convergence that appears in the particle swarm optimization algorithm, the population is adaptively divided according to the population fitness and the adaptive update strategy is introduced to enhance the ability of particles to jump out of the local optimum. Experimental results show that our proposed algorithms outperform the traditional ones, especially regarding the evaluation criterion of F1-measure and accuracy. The results on the six keel unbalanced data set the advantages of our proposed algorithms are presented.

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

Cited by 5 publications

References 40 publications

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

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

Consumer credit risk assessment: A review from the state-of-the-art classification algorithms, data traits, and learning methods

Adaptive Particle Swarm Optimization Algorithm Ensemble Model Applied to Classification of Unbalanced Data

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