Comparison of Profit-Based Multi-Objective Approaches for Feature Selection in Credit Scoring
Comparison of Profit-Based Multi-Objective Approaches for Feature Selection in Credit Scoring
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DOI：
10.3390/a14090260
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发表时间：
2021-08
期刊：
影响因子：
2.3
通讯作者：
Naomi Simumba;Suguru Okami;A. Kodaka;N. Kohtake
中科院分区：
文献类型：
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作者：
Naomi Simumba;Suguru Okami;A. Kodaka;N. Kohtake
特征选择对于信用评分过程至关重要，可以去除预测能力较低的不相关变量。传统的信用评分技术将其视为一个单独的过程，其中基于提高单个统计指标（如准确性）来选择特征;然而，最近的研究集中在有意义的业务参数（如利润）上。多个因素可能对选择过程很重要，这使得多目标优化方法成为必要。然而，多目标方法的比较性能已经知道根据测试问题和具体实现而变化。本研究采用了最近的混合非支配排序二进制蚱蜢优化算法，并比较其性能的多目标特征选择信用评分的两个流行的基准算法在这个空间。进一步的比较，以确定改变利润最大化的基础分类算法的性能的影响。实验表明，在所使用的基分类器中，神经网络分类器改进了基于利润的度量，并且最大限度地减少了种群中的平均特征数。此外，NSBGOA算法给出了相对较小的超体积，并增加了所有基础分类器的计算时间，同时给出了解决方案的最高平均目标值。很明显，基分类器对多目标优化的结果有很大的影响。因此，应该仔细考虑在场景中使用的基本分类器。
Feature selection is crucial to the credit-scoring process, allowing for the removal of irrelevant variables with low predictive power. Conventional credit-scoring techniques treat this as a separate process wherein features are selected based on improving a single statistical measure, such as accuracy; however, recent research has focused on meaningful business parameters such as profit. More than one factor may be important to the selection process, making multi-objective optimization methods a necessity. However, the comparative performance of multi-objective methods has been known to vary depending on the test problem and specific implementation. This research employed a recent hybrid non-dominated sorting binary Grasshopper Optimization Algorithm and compared its performance on multi-objective feature selection for credit scoring to that of two popular benchmark algorithms in this space. Further comparison is made to determine the impact of changing the profit-maximizing base classifiers on algorithm performance. Experiments demonstrate that, of the base classifiers used, the neural network classifier improved the profit-based measure and minimized the mean number of features in the population the most. Additionally, the NSBGOA algorithm gave relatively smaller hypervolumes and increased computational time across all base classifiers, while giving the highest mean objective values for the solutions. It is clear that the base classifier has a significant impact on the results of multi-objective optimization. Therefore, careful consideration should be made of the base classifier to use in the scenarios.