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Genetic Programming with Interval Functions and Ensemble Learning for Classification with Incomplete Data

  • Cao Truong TranEmail author
  • Mengjie Zhang
  • Bing Xue
  • Peter Andreae
Conference paper
  • 1.3k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11320)

Abstract

Missing values are an unavoidable issue in many real-world datasets. Classification with incomplete data has to be addressed carefully because inadequate treatment often leads to a big classification error. Interval genetic programming (IGP) is an approach to directly use genetic programming to evolve an effective and efficient classifier for incomplete data. This paper proposes a method to improve IGP for classification with incomplete data by integrating IGP with ensemble learning to build a set of classifiers. Experimental results show that the integration of IGP and ensemble learning to evolve a set of classifiers for incomplete data can achieve better accuracy than IGP alone. The proposed method is also more accurate than other common methods for classification with incomplete data.

Keywords

Incomplete data Classification Genetic programming Interval functions Ensemble learning 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Cao Truong Tran
    • 1
    Email author
  • Mengjie Zhang
    • 1
  • Bing Xue
    • 1
  • Peter Andreae
    • 1
  1. 1.School of Engineering and Computer ScienceVictoria University of WellingtonWellingtonNew Zealand

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