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A new transferred feature selection algorithm for customer identification

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.
0941-0643
2593-2603
Zhu, Bing
208ad68b-d16c-48ac-84b0-faefc7dca958
Niu, Yongge
c9909ef1-6cf1-4e6e-be03-0706d54f584f
Xiao, Jin
fc7b1ee2-2a91-48aa-8ed8-f72b005a50f7
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Zhu, Bing
208ad68b-d16c-48ac-84b0-faefc7dca958
Niu, Yongge
c9909ef1-6cf1-4e6e-be03-0706d54f584f
Xiao, Jin
fc7b1ee2-2a91-48aa-8ed8-f72b005a50f7
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0

Zhu, Bing, Niu, Yongge, Xiao, Jin and Baesens, Bart (2017) A new transferred feature selection algorithm for customer identification. Neural Computing and Applications, 28 (9), 2593-2603. (doi:10.1007/s00521-016-2214-y).

Record type: Article

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.

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More information

Accepted/In Press date: 19 January 2016
e-pub ahead of print date: 2 February 2016
Published date: 1 September 2017

Identifiers

Local EPrints ID: 425617
URI: http://eprints.soton.ac.uk/id/eprint/425617
ISSN: 0941-0643
PURE UUID: fc39a1de-6440-49f5-85c8-0c617da2e619
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

Date deposited: 26 Oct 2018 16:30
Last modified: 28 Apr 2022 01:53

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Contributors

Author: Bing Zhu
Author: Yongge Niu
Author: Jin Xiao
Author: Bart Baesens ORCID iD

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