The early signal of the potential risk of bank failure is imperative for various stakeholders such as management personnel, lenders, and shareholders. This study has developed a new feature selection-based data envelopment analysis (DEA) model to calculate the efficiencies and predict the stress of Indian banks. The feature selection-based data envelopment analysis (DEA) model combines feature selection methodology (a machine learning technique), with the traditional data envelopment analysis. The proposed model is able to map the DEA ranks, and the status of both failure and success of banks, and other similar decision-making units (DMUs). It also helps in solving the problems associated with the selection of appropriate input and output features as well as the time-dependent data points which usually have a lagged effect. The proposed model is applicable only when appropriate samples of past data of DMUs performance are available, whereby it maps both the input and output features with performance. Importantly, while dealing with the past data for selecting the appropriate inputs and outputs, it is imperative to select all the indicators that affect the performance of DMUs and thereby reduce the number of features using the machine learning approach. The proposed model is one of its kind, integrating a machine learning technique to a nonparametric frontier decision model.

中文翻译：

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使用特征选择数据包络分析对印度银行进行绩效评估：机器学习视角

银行倒闭潜在风险的早期信号对于管理人员、贷方和股东等各种利益相关者来说是必不可少的。本研究开发了一种新的基于特征选择的数据包络分析 (DEA) 模型来计算效率并预测印度银行的压力。基于特征选择的数据包络分析 (DEA) 模型将特征选择方法（一种机器学习技术）与传统的数据包络分析相结合。所提出的模型能够映射 DEA 等级，以及银行和其他类似决策单元 (DMU) 的失败和成功状态。它还有助于解决与选择适当的输入和输出特征以及通常具有滞后效应的时间相关数据点相关的问题。仅当 DMU 性能的过去数据的适当样本可用时，所提出的模型才适用，由此它将输入和输出特征与性能映射。重要的是，在处理过去的数据以选择合适的输入和输出时，必须选择所有影响 DMU 性能的指标，从而减少使用机器学习方法的特征数量。所提出的模型是同类模型中的一种，将机器学习技术集成到非参数前沿决策模型中。必须选择所有影响 DMU 性能的指标，从而减少使用机器学习方法的特征数量。所提出的模型是同类模型中的一种，将机器学习技术集成到非参数前沿决策模型中。必须选择所有影响 DMU 性能的指标，从而减少使用机器学习方法的特征数量。所提出的模型是同类模型中的一种，将机器学习技术集成到非参数前沿决策模型中。