SPARSE PREDICTIVE MODELING FOR BANK TELEMARKETING SUCCESS USING SMOOTH-THRESHOLD ESTIMATING EQUATIONS
SPARSE PREDICTIVE MODELING FOR BANK TELEMARKETING SUCCESS USING SMOOTH-THRESHOLD ESTIMATING EQUATIONS
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DOI：
10.5183/jjscs.1502003_217
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发表时间：
2015-12
期刊：
影响因子：
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通讯作者：
Y. Kawasaki;Masao Ueki
中科院分区：
文献类型：
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作者：
Y. Kawasaki;Masao Ueki
在本文中，我们试图建立和评估几个预测模型，以预测电话营销电话销售银行长期存款的成功，使用2008年至2013年收集的葡萄牙零售银行公开可用的数据集（Moro等人，2014年，决策支持系统）。这些数据包括与银行客户、产品和社会经济属性相关的多个预测变量，有的是数字变量，有的是分类变量。将一个分类预测变量作为多个虚拟变量来处理，增加了模型的维数，模型参数化中的冗余必须得到实际的关注。这促使我们用更简洁的建模来评估预测性能。我们采用现代的变量选择方法，包括套索法、弹性网法、平滑夹持绝对偏差法、最小凹惩罚法以及平滑阈值估计方程。除了变量选择之外，平滑阈值估计方程还可以实现预测变量的自动分组，这是进行变量选择的另一种稀疏建模方法，可以适用于某些问题，例如由分类预测变量创建的虚拟变量。每种建模方法的预测能力通过重复交叉验证实验或样本分割来评估，一个用于训练，另一个用于测试。
In this paper, we attempt to build and evaluate several predictive models to predict success of telemarketing calls for selling bank long-term deposits using a publicly available set of data from a Portuguese retail bank collected from 2008 to 2013 (Moro et al., 2014, Decision Support Systems). The data include multiple predictor variables, either numeric or categorical, related with bank client, product and social-economic attributes. Dealing with a categorical predictor variable as multiple dummy variables increases model dimensionality, and redundancy in model parameterization must be of practical concern. This motivates us to assess prediction performance with more parsimonious modeling. We apply contemporary variable selection methods with penalization including lasso, elastic net, smoothly-clipped absolute deviation, minimum concave penalty as well as the smooth-threshold estimating equation. In addition to variable selection, the smooth-threshold estimating equation can achieve automatic grouping of predictor variables, which is an alternative sparse modeling to perform variable selection and could be suited to a certain problem, e.g., dummy variables created from categorical predictor variables. Predictive power of each modeling approach is assessed by repeating cross-validation experiments or sample splitting, one for training and another for testing.