Research on Credit Card Default Prediction Based on <i>k-</i>Means SMOTE and BP Neural Network

Chen, Ying; Zhang, Ruirui

doi:10.1155/2021/6618841

Cited by 17 publications

(6 citation statements)

References 15 publications

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“…In this section, we aimed to evaluate the effectiveness of the proposed Ensemble Partition Sampling (EPS) method by comparing it to other imbalanced learning methods that have been previously proposed in the literature. To do this, we selected a number of representative methods from existing literature, including: OvA, SMOTE [26], k-means-SMOTE [27], and Bagging-RB [28]. The table 1 presents results of imbalanced learning methods using CART as a classifier on different datasets.…”

Section: A Comparison Of the Proposed Methods With Imbalanced Learnin...mentioning

confidence: 99%

Ensemble Partition Sampling (EPS) for Improved Multi-Class Classification

et al. 2023

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Classification is a commonly used technique in data mining and is applied in various fields such as sentiment analysis, fraud detection, and fault diagnosis. Multiclass classification, which involves more than two classes, is more complex than binary classification. There are mainly two ways to approach multiclass classification, one is to expand the binary classifier into a multiclass classifier through various strategies and the other is to divide the multiclass classification problem into multiple binary problems (binarization). Two popular approaches for binarization are One vs One (OvO) and One vs All (OvA). It is simpler to aggregate the outputs of all binary classifiers as the number of classifiers decreases. However, it causes an imbalance of positive and negative sample numbers, which affects the classification effect of each binary classifier. In this article, we contribute to the field of ensemble learning and multi-class classification by proposing a new method called Ensemble Partition Sampling (EPS). This article presents a new approach to multiclass classification using an ''Ensemble Partition Sampling'' method within the ''onevs-all'' (OvA) framework. The primary goal of this method is to tackle the problem of data imbalance by incorporating ensemble learning and preprocessing techniques into each binary dataset. The study found that Ensemble Partition Sampling (EPS) is the most effective method for imbalanced and multiclass imbalanced classification, outperforming other methods including OvA, SMOTE, k-means-SMOTE, Bagging-RB, DES-MI, OvO-EASY, and OvO-SMB. The study used CART, Random Forest, and SVM as classifiers, and the results consistently showed that EPS outperformed all other algorithms. Based on the findings, it can be concluded that the EPS approach is a highly effective method for improving classification performance in imbalanced and multiclass imbalanced datasets.INDEX TERMS Ensemble partition sampling (EPS), one vs one (OvO), one vs all (OvA), multi-class classification, imbalanced learning, multiclass imbalanced classification.

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Section: A Comparison Of the Proposed Methods With Imbalanced Learnin...mentioning

confidence: 99%

Ensemble Partition Sampling (EPS) for Improved Multi-Class Classification

et al. 2023

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“…Recent developments in automated risk assessment models have surpassed traditional methods in almost all cases. Machine learning techniques like BP neural networks, KNN, and SVM are prevalent (Chen & Zhang, 2021). KNN outperformed the other two algorithms on two-class classification problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Artificial Intelligence Data Mining Algorithm in Enterprise Management Risk Assessment

Zhu

2024

International Journal of Information Systems and Supply Chain Management

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For governmental and non-governmental enterprises to tackle risk management with conviction, enterprise management risk assessment (EMRA) is required. This work proposes a research methodology based on an AI-based data mining algorithm (MSVM+EFCNN) for evaluating enterprise-related risks. Initially, all the possible risk assessment indexes of the enterprise are established using a large variety of identification parameters. Then, the data mining algorithms are trained by considering the previous data for building an EMRA model. At last, the current conditions are analyzed using a cluster of risk indicators, and the risk index is identified via the EMRA model. The support vector machine is used for classification purposes, and the fuzzy-based convolutional neural network is enhanced with a genetic algorithm for creating the enterprise risk assessment. The results obtained after keen analysis and experimentation indicate that the data mining algorithms used in this work can evaluate the enterprise-related risks effectively.

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“…KMeansSMOTE introduces structure to synthetic sample generation by combining KMeans clustering with SMOTE [19]. While it attempts to create samples in a more controlled manner, it is sensitive to the choice of the K parameter and may not perform well with non-convex clusters.…”

Section: Oversampling Techniquesmentioning

confidence: 99%

Improvement of Malicious Software Detection Accuracy through Genetic Programming Symbolic Classifier with Application of Dataset Oversampling Techniques

Anđelić,

Baressi Šegota,

Car

2023

Computers

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Malware detection using hybrid features, combining binary and hexadecimal analysis with DLL calls, is crucial for leveraging the strengths of both static and dynamic analysis methods. Artificial intelligence (AI) enhances this process by enabling automated pattern recognition, anomaly detection, and continuous learning, allowing security systems to adapt to evolving threats and identify complex, polymorphic malware that may exhibit varied behaviors. This synergy of hybrid features with AI empowers malware detection systems to efficiently and proactively identify and respond to sophisticated cyber threats in real time. In this paper, the genetic programming symbolic classifier (GPSC) algorithm was applied to the publicly available dataset to obtain symbolic expressions (SEs) that could detect the malware software with high classification performance. The initial problem with the dataset was a high imbalance between class samples, so various oversampling techniques were utilized to obtain balanced dataset variations on which GPSC was applied. To find the optimal combination of GPSC hyperparameter values, the random hyperparameter value search method (RHVS) was developed and applied to obtain SEs with high classification accuracy. The GPSC was trained with five-fold cross-validation (5FCV) to obtain a robust set of SEs on each dataset variation. To choose the best SEs, several evaluation metrics were used, i.e., the length and depth of SEs, accuracy score (ACC), area under receiver operating characteristic curve (AUC), precision, recall, f1-score, and confusion matrix. The best-obtained SEs are applied on the original imbalanced dataset to see if the classification performance is the same as it was on balanced dataset variations. The results of the investigation showed that the proposed method generated SEs with high classification accuracy (0.9962) in malware software detection.

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Research on Credit Card Default Prediction Based on k-Means SMOTE and BP Neural Network

Cited by 17 publications

References 15 publications

Ensemble Partition Sampling (EPS) for Improved Multi-Class Classification

Ensemble Partition Sampling (EPS) for Improved Multi-Class Classification

Application of Artificial Intelligence Data Mining Algorithm in Enterprise Management Risk Assessment

Improvement of Malicious Software Detection Accuracy through Genetic Programming Symbolic Classifier with Application of Dataset Oversampling Techniques

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