A Hybrid Support Vector Machine Ensemble Model for Credit Scoring

Ghodselahi, Ahmad

doi:10.5120/2220-2829

Cited by 31 publications

(25 citation statements)

References 30 publications

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“…Artificial Neural Networks (ANNs) [10][11][12][13] and Support Vector Machine (SVM) [14][15][16][17][18][19] are two commonly soft computing methods used in credit scoring modelling. Recently, other methods like evolutionary algorithms [20], stochastic optimization technique and support vector machine [21] have shown promising results in terms of prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Credit scoring with a feature selection approach based deep learning

Nguyen

2016

MATEC Web of Conferences

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Abstract. In financial risk, credit risk management is one of the most important issues in financial decision-making. Reliable credit scoring models are crucial for financial agencies to evaluate credit applications and have been widely studied in the field of machine learning and statistics. Deep learning is a powerful classification tool which is currently an active research area and successfully solves classification problems in many domains. Deep Learning provides training stability, generalization, and scalability with big data. Deep Learning is quickly becoming the algorithm of choice for the highest predictive accuracy. Feature selection is a process of selecting a subset of relevant features, which can decrease the dimensionality, reduce the running time, and improve the accuracy of classifiers. In this study, we constructed a credit scoring model based on deep learning and feature selection to evaluate the applicant's credit score from the applicant's input features. Two public datasets, Australia and German credit ones, have been used to test our method. The experimental results of the real world data showed that the proposed method results in a higher prediction rate than a baseline method for some certain datasets and also shows comparable and sometimes better performance than the feature selection methods widely used in credit scoring.

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Section: Introductionmentioning

confidence: 99%

Credit scoring with a feature selection approach based deep learning

Nguyen

2016

MATEC Web of Conferences

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“…The good result reveals that a useful feature selection for choosing significant features as the input of SVM is a promising idea to improve the classification accuracy in credit scoring. Ghodselahi [20] proposed a hybrid algorithm which combines the clustering and the classification technologies. In the hybrid algorithm, the fuzzy c-means clustering is first used as a preprocess to generate homogeneous clusters which have the same features.…”

Section: A Svmmentioning

confidence: 99%

“…In [21], Yao and Lu incorporated the concept of the neighborhood rough set to the SVM to solve the credit scoring problem. Just as the idea of [20] and [21], the rough set technology is considered as a pre-process to select the optimal input features for increasing the training ability of the SVM model. In addition, the authors also use the grid search to optimize the kernel parameters.…”

Section: A Svmmentioning

confidence: 99%

Applications of artificial intelligence technologies in credit scoring: A survey of literature

Chen

Zeng

Lin

2014

2014 10th International Conference on Natural Computation (ICNC)

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Credit scoring is becoming a competitive issue with rapid growth and significant advance. Building a satisfactory credit model has attracted lots of researchers in the past decades and it is still one of the hottest research topics in the field of credit industry. This paper emphasizes on surveying the development of the artificial intelligence technologies for solving credit scoring. It covers algorithms including the support vector machines, artificial neural networks, genetic algorithms, genetic programming algorithms and their hybridization.

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“…In 2011 Ahmed et al designed a hybrid ensemble model for credit risk which combines both clustering and classification [5]. In this SVM classifiers are the members in the ensemble model.…”

Section: Introductionmentioning

confidence: 99%

Distributed Multi Class SVM for Large Data Sets

Govada

Gauri

Sahay

2015

Proceedings of the Third International Symposium on Women in Computing and Informatics

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Data mining algorithms are originally designed by assuming the data is available at one centralized site.These algorithms also assume that the whole data is fit into main memory while running the algorithm. But in today's scenario the data has to be handled is distributed even geographically. Bringing the data into a centralized site is a bottleneck in terms of the bandwidth when compared with the size of the data. In this paper for multiclass SVM we propose an algorithm which builds a global SVM model by merging the local SVMs using a distributed approach(DSVM). And the global SVM will be communicated to each site and made it available for further classification. The experimental analysis has shown promising results with better accuracy when compared with both the centralized and ensemble method. The time complexity is also reduced drastically because of the parallel construction of local SVMs. The experiments are conducted by considering the data sets of size 100s to hundred of 100s which also addresses the issue of scalability.

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A Hybrid Support Vector Machine Ensemble Model for Credit Scoring

Cited by 31 publications

References 30 publications

Credit scoring with a feature selection approach based deep learning

Credit scoring with a feature selection approach based deep learning

Applications of artificial intelligence technologies in credit scoring: A survey of literature

Distributed Multi Class SVM for Large Data Sets

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