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首页> 外文会议>IFIP WG 12.5 International Conference on artificial intelligence applications and innovations >Predicting Firms' Credit Ratings Using Ensembles of Artificial Immune Systems and Machine Learning - An Over-Sampling Approach
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Predicting Firms' Credit Ratings Using Ensembles of Artificial Immune Systems and Machine Learning - An Over-Sampling Approach

机译：使用人工免疫系统和机器学习集成预测公司的信用等级-一种过采样方法

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摘要

This paper examines the classification performance of artificial immune systems on the one hand and machine learning and neural networks on the other hand on the problem of forecasting credit ratings of firms. The problem is realized as a two-class problem, for investment and non-investment rating grades. The dataset is usually imbalanced in credit rating predictions. We address the issue by over-sampling the minority class in the training dataset. The experimental results show that this approach leads to significantly higher classification accuracy. Additionally, the use of the ensembles of classifiers makes the prediction even more accurate.
机译：本文一方面研究了人工免疫系统的分类性能，另一方面研究了机器学习和神经网络在预测公司信用等级方面的表现。对于投资和非投资评级等级，该问题被实现为两类问题。数据集通常在信用评级预测中不平衡。我们通过对训练数据集中的少数族裔进行超采样来解决该问题。实验结果表明，该方法可显着提高分类精度。另外，使用分类器集合使预测更加准确。

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