A new transferred feature selection algorithm for customer identification

A new transferred feature selection algorithm for customer identification Class imbalance brings great challenges to feature selection in customer identification, and most of the current feature selection approaches cannot produce good prediction on the minority class. A number of studies have attempted to solve this issue by using resampling techniques. However, resampling techniques only use the in-domain information and they cannot achieve good performance when the imbalance is caused by the absolute rarity of the minority class. In this paper, we focus on the issue of feature selection with class imbalance caused by absolute rarity. By introducing the idea of transfer learning, we develop a transferred feature selection method based on the group method of data handling neural networks. The proposed ensemble neural network extracts information of similar customers from related domains to deal with the information scarcity of the minority class in the target domain. Experiments are done on a real-world application using data from a cigarette company. The results indicate that the new method gives better predictive performance than other benchmark feature selection methods, especially in terms of the predictive accuracy of the minority high-value customers. At the same time, the new algorithm can help to identify important features that distinguish high-value customers from low-value ones. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neural Computing and Applications Springer Journals

A new transferred feature selection algorithm for customer identification

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Publisher
Springer London
Copyright
Copyright © 2016 by The Natural Computing Applications Forum
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery; Probability and Statistics in Computer Science; Computational Science and Engineering; Image Processing and Computer Vision; Computational Biology/Bioinformatics
ISSN
0941-0643
eISSN
1433-3058
D.O.I.
10.1007/s00521-016-2214-y
Publisher site
See Article on Publisher Site

Abstract

Class imbalance brings great challenges to feature selection in customer identification, and most of the current feature selection approaches cannot produce good prediction on the minority class. A number of studies have attempted to solve this issue by using resampling techniques. However, resampling techniques only use the in-domain information and they cannot achieve good performance when the imbalance is caused by the absolute rarity of the minority class. In this paper, we focus on the issue of feature selection with class imbalance caused by absolute rarity. By introducing the idea of transfer learning, we develop a transferred feature selection method based on the group method of data handling neural networks. The proposed ensemble neural network extracts information of similar customers from related domains to deal with the information scarcity of the minority class in the target domain. Experiments are done on a real-world application using data from a cigarette company. The results indicate that the new method gives better predictive performance than other benchmark feature selection methods, especially in terms of the predictive accuracy of the minority high-value customers. At the same time, the new algorithm can help to identify important features that distinguish high-value customers from low-value ones.

Journal

Neural Computing and ApplicationsSpringer Journals

Published: Feb 2, 2016

References

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