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A New Hybrid Model of Feature Selection for Imbalanced Data

  • Bing Zhu
  • Qingqing Deng
  • Xiaozhou He
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 241)

Abstract

The study of customer identification has the extremely vital significance to promote the core competitiveness of the enterprise. This paper focus on the problem of feature selection in customer identification. We try to solve the issue of feature selection under class imbalance and a hybrid method is proposed. We improve the data cleaning technology Tomek Links and get a new model called I-Tomlinks. Based on the using of I-Tomlinks for data preprocessing, we combine the group method of data handling (GMDH) and transfer learning together to construct a new feature selection model to solve the problem of class imbalance. The experiments show that the new method gives better predictive performance that other methods used as benchmarks. The new model provides a new tool for customer identification.

Keywords

Customer identification Feature selection Transfer learning I-Tomlinks GMDH 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Business SchoolSichuan UniversityChengduPeople’s Republic of China
  2. 2.School of Economics and ManagementBeihang UniversityBeijingPeople’s Republic of China
  3. 3.School of MathematicsSichuan UniversityChengduPeople’s Republic of China

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