使用前馈神经网络进行贷款分类

IF 3.4 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
U. I. Behunkou
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引用次数: 0

摘要

研究目的研究的目的是构建并研究使用前馈神经网络来解决贷款分类问题,并对基于神经网络的方法与现有的基于逻辑回归的方法进行比较分析。基于前馈神经网络,利用已发放贷款的历史数据,计算以下指标:成本函数、准确率、精确率、召回率,以及根据精确率和召回率值计算的衡量标准。多项式参数和主成分法用于确定所研究神经网络的最佳输入数据集。分析了数据归一化对最终结果的影响,使用两阶段法和蒙特卡罗法评估了隐藏层单元数对结果的影响,确定了均衡使用数据的效果,计算了所研究的神经网络输出层的最佳阈值,找到了隐藏层单元的最佳激活函数,研究了通过缺失值估算和使用不同程度的多项式来增加输入指标的效果,并分析了现有输入指标集的冗余性。根据研究结果,我们可以得出结论,使用直接分布网络来解决贷款分类问题是合适的。与逻辑回归相比,使用前馈神经网络实施解决方案需要更多的时间和计算资源。不过,获得的最重要的准确度和测量值要高于使用逻辑回归计算的值[1]。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Loan classification using a feed-forward neural network
Objectives. The purpose of the study is to construct and study the use of a feed-forward neural network to solve the problem of loan classification, as well as to conduct a comparative analysis of the neural networkbased approach with the existing approach based on logistic regression.Methods. Based on a feed-forward neural network using historical data on loans issued, the following metrics are calculated: cost function, Accuracy, Precision, Recall, and measure, calculated on Precision and Recall values. Polynomial parameters and the principal component method are used to determine the optimal set of input data for the studied neural network.Results. The impact of data normalization on the final result was analyzed, the influence of the number of units in the hidden layer on the outcome was evaluated using a two-stage method and the Monte Carlo method, the effect of balanced data use was determined, the optimal threshold value for output layer of the neural network under investigation was calculated, the optimal activation function for the hidden layer units was found, the effect of increasing input indicators through missing values imputation and the use of polynomials of varying degrees was studied and the redundancy in the existing set of input indicators was analyzed.Conclusion. Based on the results of the research, we can conclude that the use of a direct distribution network to solve problems of loan classification is appropriate. Compared to logistic regression, implementing a solution using a feed-forward neural network requires more time and computing resources. However, the obtained most important values of Accuracy and measure are higher than those calculated using logistic regression [1].
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来源期刊
Informatics
Informatics Social Sciences-Communication
CiteScore
6.60
自引率
6.50%
发文量
88
审稿时长
6 weeks
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