Application of new deep genetic cascade ensemble of SVM classifiers to predict the Australian credit scoring

Pławiak, Paweł; Abdar, Moloud; Acharya, U. Rajendra

doi:10.1016/j.asoc.2019.105740

Cited by 129 publications

(65 citation statements)

References 88 publications

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“…At present, deep learning is widely used in biomedical and biological information domains. For example, [32], [33] use a genetic hierarchical network and SVM to predict credit score. [34], [35] use a deep neural network to evaluate and diagnose the electrocardiogram signal.…”

Section: ) the Applications And High-performance Implementations Of mentioning

confidence: 99%

An Enhanced Framework of Generative Adversarial Networks (EF-GANs) for Environmental Microorganism Image Augmentation With Limited Rotation-Invariant Training Data

Rahaman

et al. 2020

IEEE Access

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The main obstacle to image augmentation with Generative Adversarial Networks (GANs) is the need for a large amount of training data, but this is difficult for small datasets like Environmental Microorganisms (EMs). EM image analysis plays a vital role in environmental monitoring and protection, but it is often encountered with small datasets due to the difficulty of EM image collection. To this end, we propose an Enhanced Framework of GANs (EF-GANs) that combines geometric transformation methods and GANs for EM image augmentation. First of all, the color of an EM image has an insignificant impact on its class label, based on this fact, we perform color space augmentation to the original EM images. Secondly, we train EF-GANs with augmented EM images to generate utterly new EM images. Finally, we rotate the generated samples in various directions to obtain a more natural performance. In this study, we use VGG16 and ResNet50 networks to evaluate the proposed EF-GANs on 21 different types of EMs (420 EM images). It is observed that the average precision (AP) of VGG16 increases between 4.5% and 84.1% in 20 EM classes and one class remains unchanged. The AP of Resnet50 rises between 8.7% and 38.7% in 12 EM classes and reaches 100% in two EM classes. Furthermore, to reflect the generalization performance of EF-GANs, we employ an utterly new EM image dataset (630 EM images) to test the previous VGG16 networks. We select the VGG16 networks with original and optimal settings for all the EM classes, and for testing, optimal settings for a single EM class is considered. In the 20 of 21 one-vs-rest EM image classification tasks, the AP of VGG16 increases between 1.66% and 88.1%. The results demonstrate that the proposed EF-GANs can achieve outstanding performance in augmenting single EM images with high quality and resolution, thus, to improve the APs of EM image classification.

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Section: ) the Applications And High-performance Implementations Of mentioning

confidence: 99%

An Enhanced Framework of Generative Adversarial Networks (EF-GANs) for Environmental Microorganism Image Augmentation With Limited Rotation-Invariant Training Data

Rahaman

et al. 2020

IEEE Access

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“…In this context, the development of the Z-Score was proposed in the year 1968 by Altman [32] that has been applied in many companies in the financial sector. For the application of credit scoring, in recent years, several new techniques have appeared, namely: Decision Trees [33], Artificial Neural Networks [12], Support Vector Machines [9], Rough Sets [19], Deep Learning [15], and Metaheuristic algorithms [34], among others.…”

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%

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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“…Thus, the purpose of this research is to construct a realistic framework tailored for credit scoring to optimize the hyper-parameters of neural network with swarm intelligence algorithm. This paper further benchmarks the performance of the novel framework against classical as well as hybrid or ensemble models proposed in recent literature [29][30][31][32][33][34]. This paper is to answer the following questions.…”

Section: Introductionmentioning

confidence: 99%

“…First, does the neural network with hyper-parameters determined by swarm intelligence algorithm outperform the classical credit-scoring models (i.e. logistic regression, naive Bayesian, discriminant analysis, k nearest neighbor, decision tree, support-vector machine, K-means, and random forest) and state-of-the-art models proposed in recent literature [29][30][31][32][33][34]? Second, are the fitting and generalization ability of a neural network steady after its parameters determined by swarm intelligence algorithms?…”

Section: Introductionmentioning

confidence: 99%

Optimizing hyper-parameters of neural networks with swarm intelligence: A novel framework for credit scoring

Zhang

Qiu

2020

PLoS ONE

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Neural networks are widely used in automatic credit scoring systems with high accuracy and outstanding efficiency. However, in the absence of prior knowledge, it is difficult to determine the set of hyper-parameters, which makes its application limited in practice. This paper presents a novel framework of credit-scoring model based on neural networks trained by the optimal swarm intelligence (SI) algorithm. This framework incorporates three procedures. Step 1, pre-processing, including imputation, normalization, and reordering of the samples. Step 2, training, where SI algorithms optimize hyper-parameters of back-propagation artificial neural networks (BP-ANN) with the area under curve (AUC) as the evaluation function. Step 3, test, applying the optimized model in Step 2 to predict new samples. The results show that the framework proposed in this paper searches the hyper-parameter space efficiently and finds the optimal set of hyper parameters with appropriate time complexity, which enhances the fitting and generalization ability of BP-ANN. Compared with existing credit-scoring models, the model in this paper predicts with a higher accuracy. Additionally, the model enjoys a greater robustness, for the difference of performance between training and testing phases.

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Application of new deep genetic cascade ensemble of SVM classifiers to predict the Australian credit scoring

Cited by 129 publications

References 88 publications

An Enhanced Framework of Generative Adversarial Networks (EF-GANs) for Environmental Microorganism Image Augmentation With Limited Rotation-Invariant Training Data

An Enhanced Framework of Generative Adversarial Networks (EF-GANs) for Environmental Microorganism Image Augmentation With Limited Rotation-Invariant Training Data

Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers

Optimizing hyper-parameters of neural networks with swarm intelligence: A novel framework for credit scoring

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