This paper mainly discusses the hybrid application of ensemble learning, classification, and feature selection (FS) algorithms simultaneously based on training data balancing for helping the proposed credit scoring model perform more effectively, which comprises three major stages. Firstly, it conducts preprocessing for collected credit data. Then, an efficient feature selection algorithm based on adaptive elastic net is employed to reduce the weakly related or uncorrelated variables to get high-quality training data. Thirdly, a novel ensemble strategy is proposed to make the imbalanced training data set balanced for each extreme learning machine (ELM) classifier. Finally, a new weighting method for single ELM classifiers in the ensemble model is established with respect to their classification accuracy based on generalized fuzzy soft sets (GFSS) theory. A novel cosine-based distance measurement algorithm of GFSS is also proposed to calculate the weights of each ELM classifier. To confirm the efficiency of the proposed ensemble credit scoring model, we implemented experiments with real-world credit data sets for comparison. The process of analysis, outcomes, and mathematical tests proved that the proposed model is capable of improving the effectiveness of classification in average accuracy, area under the curve (AUC), H-measure, and Brier’s score compared to all other single classifiers and ensemble approaches.

中文翻译：

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基于极限学习机和广义模糊软集的集成积分评分模型。

本文主要讨论基于训练数据平衡的集成学习，分类和特征选择（FS）算法的混合应用，以帮助所提出的信用评分模型更有效地执行，该过程包括三个主要阶段。首先，它对收集的信用数据进行预处理。然后，采用基于自适应弹性网的高效特征选择算法，减少弱相关或不相关变量，得到高质量的训练数据。第三，提出了一种新颖的集成策略，使每个极限学习机（ELM）分类器的不平衡训练数据集保持平衡。最后，基于广义模糊软集（GFSS）理论，针对集合模型中的单个ELM分类器建立了一种新的加权方法。还提出了一种基于余弦的新颖的GFSS距离测量算法，以计算每个ELM分类器的权重。为了确认所提出的整体信用评分模型的效率，我们对真实信用数据集进行了实验以进行比较。经过分析，结果和数学测试的过程证明，与所有其他单个分类器和集合相比，该模型能够提高分类准确性，平均准确度，曲线下面积（AUC），H度量和Brier得分。方法。还提出了一种基于余弦的新颖的GFSS距离测量算法，以计算每个ELM分类器的权重。为了确认所提出的整体信用评分模型的效率，我们对真实信用数据集进行了实验以进行比较。经过分析，结果和数学测试的过程证明，与所有其他单个分类器和集合相比，该模型能够提高分类准确性，平均准确度，曲线下面积（AUC），H度量和Brier得分。方法。还提出了一种基于余弦的新颖的GFSS距离测量算法，以计算每个ELM分类器的权重。为了确认所提出的整体信用评分模型的效率，我们对真实信用数据集进行了实验以进行比较。经过分析，结果和数学测试的过程证明，与所有其他单个分类器和集合相比，该模型能够提高分类准确性，平均准确度，曲线下面积（AUC），H度量和Brier得分。方法。