Credit scoring is a statistical technique that guides financial institutions to make informed decisions regarding the extension of loans to customers based on cautious examination of their historical records with the intent of reducing the organization’s operational costs and eliminate potential risks. Irrelevant attributes often degrade the classification accuracy, thus feature selection can help in dealing efficaciously with large datasets. It has been well established based on numerous studies that heterogeneous ensemble-based models have unparalleled performance among several mathematical and Artificial Intelligence-based techniques devised for the issue. This paper proposes a novel approach namely Multi-Level Classification and Cluster based Ensemble (MLCCE) that incorporates the strengths of both feature selection and ensemble-based classification. MLCCE uses the attribute dependency-based feature selection scheme followed by multi-level classification. Finally the model utilizes Particle Swarm Optimization based clustering followed by a weighted combination that corresponds to the performance of the individual classifier in different spatial regions of data. During performance evaluation, MLCCE has shown remarkable results on both the benchmark credit scoring datasets—Australian and German dataset as compared to other ensemble-based methods.

信用评分是一种统计技术，可指导金融机构根据对其历史记录的谨慎检查，就向客户提供贷款做出明智的决策，以降低组织的运营成本并消除潜在风险。不相关的属性通常会降低分类精度，因此特征选择有助于有效处理大型数据集。基于大量研究，基于异构集成的模型在为该问题设计的几种基于数学和人工智能的技术中具有无与伦比的性能，这一点已经得到很好的证实。本文提出了一种新方法，即多级分类和基于集群的集成（MLCCE），它结合了特征选择和基于集成的分类的优点。MLCCE 使用基于属性依赖的特征选择方案，然后是多级分类。最后，该模型利用基于粒子群优化的聚类，然后是加权组合，该组合对应于不同空间数据区域中单个分类器的性能。在绩效评估期间，与其他基于集成的方法相比，MLCCE 在两个基准信用评分数据集——澳大利亚和德国数据集上都显示了显着的结果。最后，该模型利用基于粒子群优化的聚类，然后是加权组合，该组合对应于不同空间数据区域中单个分类器的性能。在绩效评估期间，与其他基于集成的方法相比，MLCCE 在两个基准信用评分数据集——澳大利亚和德国数据集上都显示了显着的结果。最后，该模型利用基于粒子群优化的聚类，然后是加权组合，该组合对应于不同空间数据区域中单个分类器的性能。在绩效评估期间，与其他基于集成的方法相比，MLCCE 在两个基准信用评分数据集——澳大利亚和德国数据集上都显示了显着的结果。