Ensemble-Based Wrapper Methods for Feature Selection and Class Imbalance Learning

  • Pengyi Yang
  • Wei Liu
  • Bing B. Zhou
  • Sanjay Chawla
  • Albert Y. Zomaya
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7818)

Abstract

The wrapper feature selection approach is useful in identifying informative feature subsets from high-dimensional datasets. Typically, an inductive algorithm “wrapped” in a search algorithm is used to evaluate the merit of the selected features. However, significant bias may be introduced when dealing with highly imbalanced dataset. That is, the selected features may favour one class while being less useful to the adverse class. In this paper, we propose an ensemble-based wrapper approach for feature selection from data with highly imbalanced class distribution. The key idea is to create multiple balanced datasets from the original imbalanced dataset via sampling, and subsequently evaluate feature subsets using an ensemble of base classifiers each trained on a balanced dataset. The proposed approach provides a unified framework that incorporates ensemble feature selection and multiple sampling in a mutually beneficial way. The experimental results indicate that, overall, features selected by the ensemble-based wrapper are significantly better than those selected by wrappers with a single inductive algorithm in imbalanced data classification.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Pengyi Yang
    • 1
    • 3
  • Wei Liu
    • 2
  • Bing B. Zhou
    • 1
  • Sanjay Chawla
    • 1
  • Albert Y. Zomaya
    • 1
  1. 1.School of Information TechnologiesUniversity of SydneyAustralia
  2. 2.Dept. of Computing and Information SystemsUniversity of MelbourneAustralia
  3. 3.Garvan Institute of Medical ResearchDarlinghurstAustralia

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