Credit Scoring: A Review on Support Vector Machines and Metaheuristic Approaches

Goh, Rui Ying; Lee, Lai Soon

doi:10.1155/2019/1974794

Cited by 47 publications

(30 citation statements)

References 92 publications

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“…Ohter interesting comparatives studies are [7,21,22,29,36,37]. In addition, recent studies point to the existence of good performance of associative classifiers in solving problems of supervised classification of the financial field [26].…”

Section: Computational Intelligence Models For Financial Applicationsmentioning

confidence: 99%

“…Regarding the topic of our research, it is possible to find research papers where attempts to solve the problem of credit scoring are reported. Various supervised classification models have been used in these investigations; the use of Support Vector Machines [7][8][9], Artificial Neural Networks [10][11][12] and Classifier Ensembles [13][14][15][16], among others [17][18][19], stands out. Some of the experimental comparisons made to determine the performance of the classifiers in terms of credit assignment [20][21][22][23] exhibit, in our opinion, certain problems that prevent generalizing the published results.…”

Section: Introductionmentioning

confidence: 99%

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Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers

Rangel-Díaz-de-la-Vega

Villuendas-Rey

Yáñez-Márquez

et al. 2020

Applied Sciences

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In this paper, an experimental study was carried out to determine the influence of imbalanced datasets preprocessing in the performance of associative classifiers, in order to find the better computational solutions to the problem of credit scoring. To do this, six undersampling algorithms, six oversampling algorithms and four hybrid algorithms were evaluated in 13 imbalanced datasets referring to credit scoring. Then, the performance of four associative classifiers was analyzed. The experiments carried out allowed us to determine which sampling algorithms had the best results, as well as their impact on the associative classifiers evaluated. Accordingly, we determine that the Hybrid Associative Classifier with Translation, the Extended Gamma Associative Classifier and the Naïve Associative Classifier do not improve their performance by using sampling algorithms for credit data balancing. On the other hand, the Smallest Normalized Difference Associative Memory classifier was beneficiated by using oversampling and hybrid algorithms.

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Section: Computational Intelligence Models For Financial Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers

Rangel-Díaz-de-la-Vega

Villuendas-Rey

Yáñez-Márquez

et al. 2020

Applied Sciences

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“…For hyperparameter tuning, Grid Search (GS) has always been the conventional tuning tool for both SVM and RF. Recently, the metaheuristic approaches (MA) have shown potential as a competitive tool to tune SVM hyperparameters [ 3 ]. Some works utilized Genetic Algorithm (GA) [ 4 , 5 ] and Particle Swarm Optimization (PSO) to tune SVM [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Harmony Search–Artificial Intelligence Models in Credit Scoring

Goh

Lee

Seow

et al. 2020

Entropy

Self Cite

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Credit scoring is an important tool used by financial institutions to correctly identifydefaulters and non-defaulters. Support Vector Machines (SVM) and Random Forest (RF) are theArtificial Intelligence techniques that have been attracting interest due to their flexibility to accountfor various data patterns. Both are black-box models which are sensitive to hyperparameter settings.Feature selection can be performed on SVM to enable explanation with the reduced features,whereas feature importance computed by RF can be used for model explanation. The benefitsof accuracy and interpretation allow for significant improvement in the area of credit risk andcredit scoring. This paper proposes the use of Harmony Search (HS), to form a hybrid HS-SVM toperform feature selection and hyperparameter tuning simultaneously, and a hybrid HS-RF to tunethe hyperparameters. A Modified HS (MHS) is also proposed with the main objective to achievecomparable results as the standard HS with a shorter computational time. MHS consists of fourmain modifications in the standard HS: (i) Elitism selection during memory consideration insteadof random selection, (ii) dynamic exploration and exploitation operators in place of the originalstatic operators, (iii) a self-adjusted bandwidth operator, and (iv) inclusion of additional terminationcriteria to reach faster convergence. Along with parallel computing, MHS effectively reduces thecomputational time of the proposed hybrid models. The proposed hybrid models are comparedwith standard statistical models across three different datasets commonly used in credit scoringstudies. The computational results show that MHS-RF is most robust in terms of model performance,model explainability and computational time.

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“…It is a classical classification method based on the principle of structural risk minimization. Due to its great performance, it has attracted much attention in the field of machine learning and has been widely applied in many areas [3,4]. Similarly, the SVM-based method, Support Vector Regression (SVR), is an efficient model for regression problems, which guarantees a strong generalization ability.…”

Section: Introductionmentioning

confidence: 99%

Linear Twin Quadratic Surface Support Vector Regression

Zhai

Tian

Zhou

2020

Mathematical Problems in Engineering

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Twin support vector regression (TSVR) generates two nonparallel hyperplanes by solving a pair of smaller-sized problems instead of a single larger-sized problem in the standard SVR. Due to its efficiency, TSVR is frequently applied in various areas. In this paper, we propose a totally new version of TSVR named Linear Twin Quadratic Surface Support Vector Regression (LTQSSVR), which directly uses two quadratic surfaces in the original space for regression. It is worth noting that our new approach not only avoids the notoriously difficult and time-consuming task for searching a suitable kernel function and its corresponding parameters in the traditional SVR-based method but also achieves a better generalization performance. Besides, in order to make further improvement on the efficiency and robustness of the model, we introduce the 1-norm to measure the error. The linear programming structure of the new model skips the matrix inverse operation and makes it solvable for those huge-sized problems. As we know, the capability of handling large-sized problem is very important in this big data era. In addition, to verify the effectiveness and efficiency of our model, we compare it with some well-known methods. The numerical experiments on 2 artificial data sets and 12 benchmark data sets demonstrate the validity and applicability of our proposed method.

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Credit Scoring: A Review on Support Vector Machines and Metaheuristic Approaches

Cited by 47 publications

References 92 publications

Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers

Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers

Hybrid Harmony Search–Artificial Intelligence Models in Credit Scoring

Linear Twin Quadratic Surface Support Vector Regression

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