Hybrid credit scoring model using neighborhood rough set and multi-layer ensemble classification

Tripathi, Diwakar; Edla, Damodar Reddy; Cheruku, Ramalingaswamy

doi:10.3233/jifs-169449

Cited by 54 publications

(41 citation statements)

References 13 publications

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“…The use of a predictive tool could assist financial institutions to decide whether to grant credit to consumers who apply. Since our numerical experiments are quite encouraging, our future work is concentrated on evaluating the proposed algorithms versus relevant methodologies and frameworks addressing the credit score problem such as [27][28][29][30][31][32] and versus recently proposed advanced SSL algorithms such as [59][60][61].…”

Section: Discussionmentioning

confidence: 99%

“…In more recent works, Tripathi et al [31] proposed a hybrid credit scoring model based on dimensionality reduction by Neighborhood Rough Set algorithm for feature selection and layered ensemble classification with weighted voting approach to enhance the classification performance. They have proposed a novel classifier ranking algorithm as an underlying model for representing ranks of the classifiers based on classifier accuracy.…”

Section: Related Workmentioning

confidence: 99%

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On Ensemble SSL Algorithms for Credit Scoring Problem

et al. 2018

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Credit scoring is generally recognized as one of the most significant operational research techniques used in banking and finance, aiming to identify whether a credit consumer belongs to either a legitimate or a suspicious customer group. With the vigorous development of the Internet and the widespread adoption of electronic records, banks and financial institutions have accumulated large repositories of labeled and mostly unlabeled data. Semi-supervised learning constitutes an appropriate machine- learning methodology for extracting useful knowledge from both labeled and unlabeled data. In this work, we evaluate the performance of two ensemble semi-supervised learning algorithms for the credit scoring problem. Our numerical experiments indicate that the proposed algorithms outperform their component semi-supervised learning algorithms, illustrating that reliable and robust prediction models could be developed by the adaptation of ensemble techniques in the semi-supervised learning framework.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

On Ensemble SSL Algorithms for Credit Scoring Problem

et al. 2018

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“…Thus, the purpose of this research is to construct a realistic framework tailored for credit scoring to optimize the hyper-parameters of neural network with swarm intelligence algorithm. This paper further benchmarks the performance of the novel framework against classical as well as hybrid or ensemble models proposed in recent literature [29][30][31][32][33][34]. This paper is to answer the following questions.…”

Section: Introductionmentioning

confidence: 99%

Optimizing hyper-parameters of neural networks with swarm intelligence: A novel framework for credit scoring

Zhang

Qiu

2020

PLoS ONE

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Neural networks are widely used in automatic credit scoring systems with high accuracy and outstanding efficiency. However, in the absence of prior knowledge, it is difficult to determine the set of hyper-parameters, which makes its application limited in practice. This paper presents a novel framework of credit-scoring model based on neural networks trained by the optimal swarm intelligence (SI) algorithm. This framework incorporates three procedures. Step 1, pre-processing, including imputation, normalization, and reordering of the samples. Step 2, training, where SI algorithms optimize hyper-parameters of back-propagation artificial neural networks (BP-ANN) with the area under curve (AUC) as the evaluation function. Step 3, test, applying the optimized model in Step 2 to predict new samples. The results show that the framework proposed in this paper searches the hyper-parameter space efficiently and finds the optimal set of hyper parameters with appropriate time complexity, which enhances the fitting and generalization ability of BP-ANN. Compared with existing credit-scoring models, the model in this paper predicts with a higher accuracy. Additionally, the model enjoys a greater robustness, for the difference of performance between training and testing phases.

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“…Other works [16][17][18][19] presented a comparative study on various ensemble methods, such as bagging, boosting, random subspace, decorate, and rotation forest, for credit scoring. Other works [16][17][18][19] presented a comparative study on various ensemble methods, such as bagging, boosting, random subspace, decorate, and rotation forest, for credit scoring.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid credit scoring model based on ensemble feature selection and multilayer ensemble classification

Tripathi

Edla

Cheruku

et al. 2019

Computational Intelligence

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Credit scoring focuses on the development of empirical models to support the financial decision-making processes of financial institutions and credit industries. It makes use of applicants' historical data and statistical or machine learning techniques to assess the risk associated with an applicant. However, the historical data may consist of redundant and noisy features that affect the performance of credit scoring models. The main focus of this paper is to develop a hybrid model, combining feature selection and a multilayer ensemble classifier framework, to improve the predictive performance of credit scoring. The proposed hybrid credit scoring model is modeled in three phases. The initial phase constitutes preprocessing and assigns ranks and weights to classifiers. In the next phase, the ensemble feature selection approach is applied to the preprocessed dataset. Finally, in the last phase, the dataset with the selected features is used in a multilayer ensemble classifier framework. In addition, a classifier placement algorithm based on the Choquet integral value is designed, as the classifier placement affects the predictive performance of the ensemble framework. The proposed hybrid credit scoring model is validated on real-world credit scoring datasets, namely, Australian, Japanese, German-categorical, and German-numerical datasets.

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Hybrid credit scoring model using neighborhood rough set and multi-layer ensemble classification

Cited by 54 publications

References 13 publications

On Ensemble SSL Algorithms for Credit Scoring Problem

On Ensemble SSL Algorithms for Credit Scoring Problem

Optimizing hyper-parameters of neural networks with swarm intelligence: A novel framework for credit scoring

A novel hybrid credit scoring model based on ensemble feature selection and multilayer ensemble classification

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