Bagging Probit Models for Unbalanced Classification

Strategic Advancements in Utilizing Data Mining and Warehousing Technologies Pub Date : 1900-01-01 DOI:10.4018/978-1-60566-717-1.CH017

Hualin Wang, Xiaogang Su

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引用次数: 5

Abstract

The 11th Pacific-Asia Knowledge Discovery and Data Mining Conference (PAKDD 2007) hosted a data mining competition, co-organized by the Singapore Institute of Statistics. The data set is from a consumer finance company with the aim of finding solutions for a cross-selling business problem. The company currently has two databases, one for credit card holders and the other for home loan (mortgage) customers and they would like to make use of this opportunity to cross-sell home loans to its credit card holders. Thus, it is of their keen interest to have an effective scoring model for predicting potential cross-sell take-ups. The training dataset contains information on 40,700 customers with 40 input variables, most of which are related to the point of application for the company’s credit card, plus a binary target variable indicating the home loan take-up status. This is a sample of customers who opened a new credit card with the company within a specific 2-year period and did not have an existing home loan with the company. The binary target variable has a value of 1 if the customer then opened a home loan with the company within 12 months after opening the credit abstract

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不平衡分类的Bagging Probit模型

第十一届亚太知识发现和数据挖掘会议(PAKDD 2007)主办了一项数据挖掘竞赛，由新加坡统计研究所协办。该数据集来自一家消费金融公司，目的是为交叉销售业务问题找到解决方案。该公司目前有两个数据库，一个用于信用卡持有人，另一个用于住房贷款(抵押贷款)客户，他们希望利用这个机会向信用卡持有人交叉销售住房贷款。因此，有一个有效的评分模型来预测潜在的交叉销售占有率是他们的浓厚兴趣。训练数据集包含40,700名客户的信息，其中有40个输入变量，其中大多数与公司信用卡的申请点有关，另外还有一个指示房屋贷款占用状态的二进制目标变量。这是一个客户的样本谁开了一个新的信用卡与该公司在一个特定的2年期间，并没有现有的房屋贷款与该公司。如果客户在开立信用摘要后的12个月内向该公司开立了住房贷款，则二元目标变量的值为1

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Strategic Advancements in Utilizing Data Mining and Warehousing Technologies

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