A novel multi-stage ensemble model with enhanced outlier adaptation for credit scoring

Zhang, Wenyu; Yang, Dongqi; Ablanedo-Rosas, José Humberto; Wu, Xin; Lou, Yu

doi:10.1016/j.eswa.2020.113872

Cited by 45 publications

(32 citation statements)

References 45 publications

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“…To obtain strong performance for credit scoring, Zhang et al ( 2021 ) suggested an unique multi-stage ensemble model with enhanced outlier adaptability. To mitigate the negative effects of outliers in noisy credit datasets, a local outlier factor algorithm is enhanced with the bagging strategy to detect potential outliers and then boost them back into the training set to create an outlier-adapted training set that improves the outlier adaptability of base classifiers.…”

Section: Related Workmentioning

confidence: 99%

BACS: blockchain and AutoML-based technology for efficient credit scoring classification

Yang

Qiao

et al. 2022

Ann Oper Res

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Credit evaluation is of high scientific significance and practical use, especially in today’s plight of the world suffering from the COVID-19 epidemic. However, due to the difficulties inherent in credit scoring model building which involves a large number of data mining steps and requires a lot of time to process the data and build the model, efficient and accurate credit scoring methods are are urgently required. Aiming to solve this problem, we propose BACS, an blockchain and automated machine learning based classification model using credit dataset so that the credit modelling processes are performed in the pipeline in an automated manner to eventually obtain the classification results of credit scoring. BACS scheme consists of credit data storage to blockchain, feature extraction, feature selection, modelling algorithm and hyperparameter optimization, and model evaluation. Firstly, we propose a mechanism for credit data management and storage using blockchain to ensure that the entire credit scoring system is traceable and that the information of each scoring candidate is securely, efficiently and tamper-proofly stored on the blockchain nodes. Next, we design a pipeline using a random forest model to effectively integrate the key steps of credit data feature extraction, feature selection, credit model construction, and model evaluation. The experimental results demonstrate that our proposed automated machine learning-based credit scoring classification scheme BACS can assess the credit condition efficiently and accurately.

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Section: Related Workmentioning

confidence: 99%

BACS: blockchain and AutoML-based technology for efficient credit scoring classification

Yang

Qiao

et al. 2022

Ann Oper Res

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“…Tripathi et al [13] used the ensemble feature selection approach on datasets to which in the next stage a multilayer ensemble classifier was applied to enhance the performance for scoring credit risks. Zhang et al [14] employed, in order to extract main features, first gradient boosting decision trees (GBDT) for feature transformation and one-hot encoding and then Chi-square statistics to calculate the correlation between features and to select the main ones.…”

Section: Machine Learning and Credit Scoring: A Review Of Literaturementioning

confidence: 99%

Feature Selection in a Credit Scoring Model

Laborda

Ryoo

2021

Mathematics

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This paper proposes different classification algorithms—logistic regression, support vector machine, K-nearest neighbors, and random forest—in order to identify which candidates are likely to default for a credit scoring model. Three different feature selection methods are used in order to mitigate the overfitting in the curse of dimensionality of these classification algorithms: one filter method (Chi-squared test and correlation coefficients) and two wrapper methods (forward stepwise selection and backward stepwise selection). The performances of these three methods are discussed using two measures, the mean absolute error and the number of selected features. The methodology is applied for a valuable database of Taiwan. The results suggest that forward stepwise selection yields superior performance in each one of the classification algorithms used. The conclusions obtained are related to those in the literature, and their managerial implications are analyzed.

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“…Recently, many ensemble and hybrid techniques with high predictive performance have been proposed for credit scoring application [36][37][38][39][40]. The ensemble procedure applies to methods of combining classifiers, whereby multiple techniques are employed to solve the same problem in order to boost credit scoring performance.…”

Section: Benchmark Classification Algorithmsmentioning

confidence: 99%

A Partially Interpretable Adaptive Softmax Regression for Credit Scoring

et al. 2021

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Credit scoring is a process of determining whether a borrower is successful or unsuccessful in repaying a loan using borrowers’ qualitative and quantitative characteristics. In recent years, machine learning algorithms have become widely studied in the development of credit scoring models. Although efficiently classifying good and bad borrowers is a core objective of the credit scoring model, there is still a need for the model that can explain the relationship between input and output. In this work, we propose a novel partially interpretable adaptive softmax (PIA-Soft) regression model to achieve both state-of-the-art predictive performance and marginally interpretation between input and output. We augment softmax regression by neural networks to make it adaptive for each borrower. Our PIA-Soft model consists of two main components: linear (softmax regression) and non-linear (neural network). The linear part explains the fundamental relationship between input and output variables. The non-linear part serves to improve the prediction performance by identifying the non-linear relationship between features for each borrower. The experimental result on public benchmark datasets shows that our proposed model not only outperformed the machine learning baselines but also showed the explanations that logically related to the real-world.

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A novel multi-stage ensemble model with enhanced outlier adaptation for credit scoring

Cited by 45 publications

References 45 publications

BACS: blockchain and AutoML-based technology for efficient credit scoring classification

BACS: blockchain and AutoML-based technology for efficient credit scoring classification

Feature Selection in a Credit Scoring Model

A Partially Interpretable Adaptive Softmax Regression for Credit Scoring

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