An Incremental Learning Ensemble Method for Imbalanced Credit Scoring

Tian, Jin; Xin-ye, Liu; Li, Minqiang

doi:10.1109/ssci44817.2019.9002821

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…28 Pławiak et al 29 proposed a 29-layer network with different machine learning algorithms (deep genetic hierarchical network of learners) with limitations in extreme complexity and the long-term training. Ensembles of algorithms were also efficiently combined and applied to the German Credit Score dataset, such as the incremental learning ensemble method (ILEM) 30 and CF-GA-Ens (clustering with fuzzy assignment-genetic algorithm-ensemble learning) 31 and the novel tree-based overfitting-cautious heterogeneous ensemble model (OCHE). 32 Some of the newer studies also applied novel machine learning techniques to German credit scoring models: step-wise multi-grained augmented gradient boosting decision trees, 33 dynamic 1-nearest neighbor 34 and Cost-sensitive Neural Network Ensemble.…”

Section: Other Deep Learning Techniques In Credit Scoringmentioning

confidence: 99%

“…Pławiak et al 29 proposed a 29‐layer network with different machine learning algorithms (deep genetic hierarchical network of learners) with limitations in extreme complexity and the long‐term training. Ensembles of algorithms were also efficiently combined and applied to the German Credit Score dataset, such as the incremental learning ensemble method (ILEM) 30 and CF‐GA‐Ens (clustering with fuzzy assignment—genetic algorithm—ensemble learning) 31 and the novel tree‐based overfitting‐cautious heterogeneous ensemble model (OCHE) 32 …”

Section: Literature Reviewmentioning

confidence: 99%

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Intelligent credit scoring using deep learning methods

Gicić

Ðonko

Subaşı

2023

Concurrency and Computation

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Summary Credit scoring is one the most important parts of credit risk management in reducing the risk of client defaults and bankruptcies. Deep learning has received much attention in recent years, but it has not been implemented so intensively in credit scoring compared to other financial domains. In this article, stacked unidirectional and bidirectional LSTM (long short‐term memory) networks as a complex area of deep learning are applied in solving credit scoring problems for the first time. The proposed robust model exploits the full potential of the three‐layer stacked LSTM and BDLSTM (bidirectional LSTM) architecture with the treatment and modeling of public datasets in a novel way since credit scoring is not a time sequence problem. Attributes of each loan instance were transformed into a sequence of the matrix with a fixed sliding window approach with a one‐time step. Our proposed models outperform existing and much more complex deep learning solutions thus we succeeded in preserving simplicity. In this article, measures of different types are employed to carry out consistent conclusions. The results by applying three hidden layers on the German Credit dataset showed an accuracy of 87.19%, for Kaggle dataset accuracy reached 93.69%, and for Microcredit dataset accuracy of 97.80%.

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