Classification Restricted Boltzmann Machine for comprehensible credit scoring model

Tomczak, Jakub M.; Zięba, Maciej

doi:10.1016/j.eswa.2014.10.016

Cited by 78 publications

(40 citation statements)

References 17 publications

(20 reference statements)

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“…Johansson et al (Johansson et al, 2004) introduced a rule extraction method based on genetic programming, which can transform ANN models in different interpretable models. Tomczak et al (Tomczak & Zieba, 2015) created scoring tables from ANNs. Some approaches try to simplify black box models like SVMs.…”

Section: A C C E P T E D Mmentioning

confidence: 99%

Demonstrating non-inferiority of easy interpretable methods for insolvency prediction

Obermann

Waack

2015

Expert Systems with Applications

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Section: A C C E P T E D Mmentioning

confidence: 99%

Demonstrating non-inferiority of easy interpretable methods for insolvency prediction

Obermann

Waack

2015

Expert Systems with Applications

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“…Therefore, domain experts cannot understand the mechanism behind the model, so to improve it, further research is necessary. ()…”

Section: Introductionmentioning

confidence: 99%

A hybrid interpretable credit card users default prediction model based on RIPPER

Ding

Pan

2018

Concurrency and Computation

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Summary With the vigorous development of the financial sector, financial risks are showing a tendency toward diversification, particularly regarding the customer credit risk of commercial banks. Therefore, the customer's credit risk is being considered by financial institutions, and a credit evaluating model has emerged as a result. Currently, research has concentrated on enhancing the precision of the model, ignoring the interpretability, which makes it difficult to apply in the industry. Compared to precision, studies related to the interpretable model are limited. In our previous work, we did not consider model operation time and stability. Therefore, this study proposes a hybrid model based on the RIPPER algorithm. First, according to the characteristics of credit card data sets, targeted special data pretreatment methods are proposed. Next, the RELIEF method for feature selection removes the redundant features and further improves the interpretability of the model. Then, to address the problem of the imbalanced distribution of credit card data sets, a synthetic minority class sampling algorithm is used to equalize the samples. Finally, default credit card users are predicted by taking advantage of the rules generated by the RIPPER algorithm. To test the performance of the model, we used Taiwanese credit card customer data for empirical research. We considered model accuracy and interpretability when comparing the proposed SPR‐RIPPER model with the existing mainstream models. The results of the experiments indicate that the proposed model achieves acceptable results. This study demonstrates that the proposed credit card user default prediction model, SPR‐RIPPER, has practical application value.

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“…the first layer is the visible layer containing inputs; the second layer is a hidden layer with hidden units, and the third is the binary output. The advantage of this as discussed by [15] is that it can represent any distribution over binary vectors and the probability can be improved by increasing the number of hidden units. The approach adopted to train the model are two, the generative approach or the discriminative approach.…”

Section: Restricted Boltzmann Machinementioning

confidence: 99%

“…The same transformation is applied to the class label and test data. b) Restricted boltzmann machine For the RBM, the dataset is converted into binary as proposed by [15] whereby each feature is split into a binary value. This is a laborious process as for each of the features defined in the datasets, the representative value in binary is required.…”

Section: A) Convolution Neural Networkmentioning

confidence: 99%

Comparison of Deep Learning Algorithms to Predict Customer Churn within a Local Retail Industry

Dingli¹,

Marmara²,

Fournier³

2017

IJMLC

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Abstract-A top priority in any business is a constant need to increase revenue and profitability. One of the causes for a decrease in profits is when current customers stop transacting. When a customer leaves or churns from a business, the opportunity for potential sales or cross selling is lost. If a customer leaves the business without any form of advice, the company may find it hard to respond and take corrective action.

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Classification Restricted Boltzmann Machine for comprehensible credit scoring model

Cited by 78 publications

References 17 publications

Demonstrating non-inferiority of easy interpretable methods for insolvency prediction

Demonstrating non-inferiority of easy interpretable methods for insolvency prediction

A hybrid interpretable credit card users default prediction model based on RIPPER

Comparison of Deep Learning Algorithms to Predict Customer Churn within a Local Retail Industry

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