基于混合深度机器学习算法的信用风险预测模型

4区 计算机科学 Q3 Computer Science Scientific Programming Pub Date : 2023-11-06 DOI:10.1155/2023/6675425
Tamiru Melese, Tesfahun Berhane, Abdu Mohammed, Assaye Walelgn
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引用次数: 0

摘要

信用风险预测是银行业最具挑战性的任务之一。本文提出了一种卷积神经网络支持向量机/随机森林/决策树(CNN-SVM /RF/DT)混合模型,用于有效的信用风险预测。我们提出了四个分类器来发展模型。使用端到端过程训练具有soft-max的完全连接层构成第一个分类器,通过删除最后一个具有soft-max的完全连接层,其他三个分类器- SVM, RF和DT分类器堆叠在平坦层之后。在整个测试过程中,考虑并微调了不同的参数值,以选择合适的参数。根据实验结果,全连接CNN和SVM、DT、RF混合CNN的预测性能分别为86.70%、98.60%、96.90%和95.50%。结果表明,我们提出的混合方法在预测信用风险的能力上超过了全连接CNN。
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Credit-Risk Prediction Model Using Hybrid Deep—Machine-Learning Based Algorithms
Credit-risk prediction is one of the challenging tasks in the banking industry. In this study, a hybrid convolutional neural network—support vector machine/random forest/decision tree (CNN—SVM/RF/DT) model has been proposed for efficient credit-risk prediction. We proposed four classifiers to develop the model. A fully connected layer with soft-max trained using an end-to-end process makes up the first classifier and by deleting the final fully connected with soft-max layer, the other three classifiers—a SVM, RF, and DT classifier stacked after the flattening layer. Different parameter values were considered and fine-tuned throughout testing to select appropriate parameters. In accordance with the experimental findings, a fully connected CNN and a hybrid CNN with SVM, DT, and RF, respectively, achieved a prediction performance of 86.70%, 98.60%, 96.90%, and 95.50%. According to the results, our suggested hybrid method exceeds the fully connected CNN in its ability to predict credit risk.
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来源期刊
Scientific Programming
Scientific Programming 工程技术-计算机:软件工程
自引率
0.00%
发文量
1059
审稿时长
>12 weeks
期刊介绍: Scientific Programming is a peer-reviewed, open access journal that provides a meeting ground for research results in, and practical experience with, software engineering environments, tools, languages, and models of computation aimed specifically at supporting scientific and engineering computing. The journal publishes papers on language, compiler, and programming environment issues for scientific computing. Of particular interest are contributions to programming and software engineering for grid computing, high performance computing, processing very large data sets, supercomputing, visualization, and parallel computing. All languages used in scientific programming as well as scientific programming libraries are within the scope of the journal.
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