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Multi-Objective Genetic Programming for Classification with Unbalanced Data

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AI 2009: Advances in Artificial Intelligence (AI 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5866))

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Abstract

Existing learning and search algorithms can suffer a learning bias when dealing with unbalanced data sets. This paper proposes a Multi-Objective Genetic Programming (MOGP) approach to evolve a Pareto front of classifiers along the optimal trade-off surface representing minority and majority class accuracy for binary class imbalance problems. A major advantage of the MOGP approach is that by explicitly incorporating the learning bias into the search algorithm, a good set of well-performing classifiers can be evolved in a single experiment while canonical (single-solution) Genetic Programming (GP) requires some objective preference be a priori built into a fitness function. Our results show that a diverse set of solutions was found along the Pareto front which performed as well or better than canonical GP on four class imbalance problems.

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Author information

Authors and Affiliations

  1. School of Engineering and Computer Science, Victoria University of Wellington, New Zealand

    Urvesh Bhowan, Mengjie Zhang & Mark Johnston

Authors
  1. Urvesh Bhowan
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  2. Mengjie Zhang
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  3. Mark Johnston
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Editor information

Editors and Affiliations

  1. Clayton School of Information Technology, Monash University, 3800, Clayton, VIC, Australia

    Ann Nicholson

  2. School of Computer Science and Information Technology, RMIT University, 3001, Melbourne, VIC, Australia

    Xiaodong Li

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Bhowan, U., Zhang, M., Johnston, M. (2009). Multi-Objective Genetic Programming for Classification with Unbalanced Data. In: Nicholson, A., Li, X. (eds) AI 2009: Advances in Artificial Intelligence. AI 2009. Lecture Notes in Computer Science(), vol 5866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10439-8_38

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  • DOI: https://doi.org/10.1007/978-3-642-10439-8_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10438-1

  • Online ISBN: 978-3-642-10439-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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