Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods

Cross-Company Churn Prediction (CCCP) is a domain of research where one company (target) is lacking enough data and can use data from another company (source) to predict customer churn successfully. To support CCCP, the cross-company data is usually transformed to a set of similar normal distribution of target company data prior to building a CCCP model. However, it is still unclear which data transformation method is most effective in CCCP. Also, the impact of data transformation methods on CCCP model performance using different classifiers have not been comprehensively explored in the telecommunication sector. In this study, we devised a model for CCCP using data transformation methods (i.e., log, z-score, rank and box-cox) and presented not only an extensive comparison to validate the impact of these transformation methods in CCCP, but also evaluated the performance of underlying baseline classifiers (i.e., Naive Bayes (NB), K-Nearest Neighbour (KNN), Gradient Boosted Tree (GBT), Single Rule Induction (SRI) and Deep learner Neural net (DP)) for customer churn prediction in telecommunication sector using the above mentioned data transformation methods. We performed experiments on publicly available datasets related to the telecommunication sector. The results demonstrated that most of the data transformation methods (e.g., log, rank, and box-cox) improve the performance of CCCP significantly. However, the Z-Score data transformation method could not achieve better results as compared to the rest of the data transformation methods in this study. Moreover, it is also investigated that the CCCP model based on NB outperform on transformed data and DP, KNN and GBT performed on the average, while SRI classifier did not show significant results in term of the commonly used evaluation measures (i.e., probability of detection, probability of false alarm, area under the curve and g-mean).     

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现代远程开放教育教与学的行为关系研究

现代远程开放教育模式下的教与学的行为关系,是否朝良性循环的方向发展,是推动教学改革的关键。怎样营造良好的教学行为关系、提高远程开放教育的教育质量、使远程开放教育教与学的关系继续朝健康的方向发展,文章在这方面进行了探索和研究。     

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简单介绍了中国科学计量指标数据库, 详细阐述了如何进行数据分析和系统设计, 以及指标数据库的功能实现。     

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毕业论文是培养学生综合应用专业知识分析和解决工作实践中的具体问题的重要的实践性教学环节。开放教育金融本科学员的毕业论文质量存在诸多问题,原因不仅有学员自身的,也包括指导教师、办学单位和试点教学管理等方面的,因而要提高开放教育金融本科毕业论文质量,必须从全方位入手。     

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20世纪20年代苏俄政府在华策略经历了一个从联合吴佩孚到促使吴与孙中山联合再到联合孙中山的过程。这种策略客观上促进了以京汉铁路工人罢工为标志的中国北方工人运动发展的高潮，也在一定程度上影响了中国共产党人对国内政治形势的判断及其活动。     

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强化教学教务过程的管理是现代远程开放教育的新课题,要根据现代远程开放教育的特点,建立健全一套完整的管理体系,从把好入学关到教学全过程,各种管理制度的制定和执行等,都要纳入规范化、现代化、制度化的轨道,以确保现代远程开放教育的质量。     

开放数字环境下的参考文献链接

介绍开放式分布式数字信息环境中参考文献链接机制的基本概念和实现框架 ,并具体讨论相关的实施技术和范例系统。     

数据挖掘在数字图书馆中的应用

在描述数据挖掘技术与方法基础之上，探讨了数据挖掘在数字图书馆中的应用空间以及其所具有的巨大应用价值。