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Comparative study of individual and ensemble methods of classification for credit scoring

机译：个体和整体信用评分方法的比较研究

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Credit Scoring is the primary method for classifying loan applicants into two classes, namely credible payers and defaulters. In general, credit score is the primary indicator of creditworthiness of the person. This credit scoring technique is used by banks and other money lenders to build a probabilistic predictive model, called a scorecard for estimating the probability of defaulters. In the current global scenario, credit scoring is a major tool for risk evaluation and risk management for all the existing and emerging economies. With the introduction of Basel II Accord, Credit scoring has gained much significance in retail credit industry. In this paper, we performed an extensive comparative in order to classify the credit scoring and identification of best classifier. Furthermore, we used two different categories of classifiers i.e. individual and ensemble. Identification of optimal machine-learning methods for credit scoring applications is a crucial step towards stable creditworthiness of the person. Different parameters Accuracy, AUC, F-measure, precision and recall are used for the evaluation of the results.
机译：信用评分是将贷款申请人分为可靠的付款人和违约人两类的主要方法。通常，信用评分是个人信用度的主要指标。银行和其他放债人使用这种信用评分技术来建立概率预测模型，称为记分卡，用于估计违约概率。在当前的全球情况下，信用评分是所有现有和新兴经济体进行风险评估和风险管理的主要工具。随着《巴塞尔协议II》的推出，信用评分在零售信用行业中已变得越来越重要。在本文中，我们进行了广泛的比较，以对信用评分和最佳分类器进行分类。此外，我们使用了两个不同类别的分类器，即个人分类和整体分类。识别用于信用评分应用的最佳机器学习方法是实现个人稳定信用的关键一步。评估结果使用不同的参数准确性，AUC，F量度，精度和查全率。

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