NDLI: Improving churn prediction in telecommunications using complementary fusion of multilayer features based on factorization and construction

Content Provider	IEEE Xplore Digital Library
Author	Qiuhua Shen Hong Li Qin Liao Wei Zhang Kalilou, K.
Copyright Year	2014
Description	Author affiliation: Sch. of Inf. Sci. & Eng., Central South Univ., Changsha, China (Qiuhua Shen; Hong Li; Qin Liao; Kalilou, K.) \|\| Sch. of Archit., Hunan Univ., Changsha, China (Wei Zhang)
Abstract	High dimensional and unbalanced datasets are the main problems which prevent from achieving ideally churn prediction performance. Features selection is necessary to be adopted to enhance the model performance. A new predicting framework is proposed in this paper which uses complementary fusion of the multilayer features. Several subsets and new features were acquired according to feature factorization and feature construction respectively. The effective features were selected by multilayer complementary fusion which according to the contribution of feature subsets and new features. In this way, the imbalance defects of class distributions can be fixed, prediction accuracy can be improved and system stability can be reinforced. Five data mining models were applied in customer churn. Experimental results demonstrated that the method we proposed could preferably overcome the inelasticity of traditional feature selection algorithms, and more effective than those existing methods in telecommunications industry. Furthermore, we found optimal fusion with prediction model for customer churn prediction in telecommunications industry through exploring the advantages and limitations of each feature subset and prediction techniques.
Starting Page	2250
Ending Page	2255
File Size	184520
Page Count	6
File Format	PDF
ISBN	9781479937073
e-ISBN	9781479937080
DOI	10.1109/CCDC.2014.6852544
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-31
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Prediction techniques Nonhomogeneous media Educational institutions Telecommunications Electronic mail Feature Selection Accuracy Complementary Fusion Feature Construction Prediction algorithms Feature extraction Feature Factorization Customer Churn Prediction
Content Type	Text
Resource Type	Article

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