A novel multi-stage hybrid model with enhanced multi-population niche genetic algorithm: An application in credit scoring

With the in-depth application of artificial intelligence technology in the financial field, credit scoring models constructed by machine learning algorithms have become mainstream. However, the high-dimensional and complex attribute features of the borrower pose challenges to the predictive competence of the model. This paper proposes a hybrid model with a novel feature selection method and an enhanced voting method for credit scoring. First, a novel feature selection combined method based on a genetic algorithm (FSCM-GA) is proposed, in which different classifiers are used to select features in combination with a genetic algorithm and combine them to generate an optimal feature subset. Furthermore, an enhanced voting method (EVM) is proposed to integrate classifiers, with the aim of improving the classification results in which the prediction probability values are close to the threshold. Finally, the predictive competence of the proposed model was validated on three public datasets and five evaluation metrics (accuracy, AUC, F-score, Log loss and Brier score). The comparative experiment and significance test results confirmed the good performance and robustness of the proposed model.

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A novel multi-stage ensemble model with multiple K-means-based selective undersampling: An application in credit scoring

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201954 ◽

2021 ◽

Vol 40 (5) ◽

pp. 9471-9484

Author(s):

Yilun Jin ◽

Yanan Liu ◽

Wenyu Zhang ◽

Shuai Zhang ◽

Yu Lou

Keyword(s):

Machine Learning ◽

Predictive Accuracy ◽

Credit Scoring ◽

Imbalanced Data ◽

Ensemble Model ◽

Selective Sampling ◽

Machine Learning Methods ◽

Multi Stage ◽

Proposed Model ◽

New Feature

With the advancement of machine learning, credit scoring can be performed better. As one of the widely recognized machine learning methods, ensemble learning has demonstrated significant improvements in the predictive accuracy over individual machine learning models for credit scoring. This study proposes a novel multi-stage ensemble model with multiple K-means-based selective undersampling for credit scoring. First, a new multiple K-means-based undersampling method is proposed to deal with the imbalanced data. Then, a new selective sampling mechanism is proposed to select the better-performing base classifiers adaptively. Finally, a new feature-enhanced stacking method is proposed to construct an effective ensemble model by composing the shortlisted base classifiers. In the experiments, four datasets with four evaluation indicators are used to evaluate the performance of the proposed model, and the experimental results prove the superiority of the proposed model over other benchmark models.

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Research and Application of a New Hybrid Forecasting Model Based on Genetic Algorithm Optimization: A Case Study of Shandong Wind Farm in China

Mathematical Problems in Engineering ◽

10.1155/2015/740490 ◽

2015 ◽

Vol 2015 ◽

pp. 1-14 ◽

Cited By ~ 3

Author(s):

Ping Jiang ◽

Xiaofei Li ◽

Yao Dong

Keyword(s):

Genetic Algorithm ◽

Wind Speed ◽

Hybrid Model ◽

Hybrid System ◽

Wind Farm ◽

Evaluation Criteria ◽

Renewable Resource ◽

Original Data ◽

Determine Parameter ◽

Forecasting Method

With the increasing depletion of fossil fuel and serious destruction of environment, wind power, as a kind of clean and renewable resource, is more and more connected to the power system and plays a crucial role in power dispatch of hybrid system. Thus, it is necessary to forecast wind speed accurately for the operation of wind farm in hybrid system. In this paper, we propose a hybrid model called EEMD-GA-FAC/SAC to forecast wind speed. First, the Ensemble empirical mode decomposition (EEMD) can be applied to eliminate the noise of the original data. After data preprocessing, first-order adaptive coefficient forecasting method (FAC) or second-order adaptive coefficient forecasting method (SAC) can be employed to do forecast. It is significant to select optimal parameters for an effective model. Thus, genetic algorithm (GA) is used to determine parameter of the hybrid model. In order to verify the validity of the proposed model, every ten-minute wind speed data from three observation sites in Shandong Peninsula of China and several error evaluation criteria can be collected. Through comparing with traditional BP, ARIMA, FAC, and SAC model, the experimental results show that the proposed hybrid model EEMD-GA-FAC/SAC has the best forecasting performance.

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A novel multi-stage ensemble model for credit scoring based on synthetic sampling and feature transformation

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-211467 ◽

2021 ◽

pp. 1-16

Author(s):

Fang He ◽

Wenyu Zhang ◽

Zhijia Yan

Keyword(s):

Credit Scoring ◽

Imbalanced Data ◽

Transformation Method ◽

Classification Performance ◽

Ensemble Model ◽

Feature Transformation ◽

Learning Methods ◽

Scoring Model ◽

Multi Stage ◽

Credit Scoring Model

Credit scoring has become increasingly important for financial institutions. With the advancement of artificial intelligence, machine learning methods, especially ensemble learning methods, have become increasingly popular for credit scoring. However, the problems of imbalanced data distribution and underutilized feature information have not been well addressed sufficiently. To make the credit scoring model more adaptable to imbalanced datasets, the original model-based synthetic sampling method is extended herein to balance the datasets by generating appropriate minority samples to alleviate class overlap. To enable the credit scoring model to extract inherent correlations from features, a new bagging-based feature transformation method is proposed, which transforms features using a tree-based algorithm and selects features using the chi-square statistic. Furthermore, a two-layer ensemble method that combines the advantages of dynamic ensemble selection and stacking is proposed to improve the classification performance of the proposed multi-stage ensemble model. Finally, four standardized datasets are used to evaluate the performance of the proposed ensemble model using six evaluation metrics. The experimental results confirm that the proposed ensemble model is effective in improving classification performance and is superior to other benchmark models.

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