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Classification and Survival Analysis Using Multi-objective Evolutionary Algorithms

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Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases

Part of the book series: Studies in Computational Intelligence ((SCI,volume 98))

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Model extraction from data has at least three objectives. It aims to produce accurate, comprehensible and interesting models. Hence, multi-objective evolutionary algorithms are a natural choice to tackle this problem. They are capable of optimizing several incommensurable objectives in as single run without making any assumptions about the importance of each objective. This chapter proposes several multi-objective evolutionary algorithms to tackle three different model extraction tasks. The first approach performs supervised classification whilst overcoming some of the shortcomings of existing approaches. The second and third approach tackle two survival analysis problems. All approaches are evaluated on artificial, benchmark and real-world medical data sets.

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Author information

Authors and Affiliations

  1. National Center for Zoonosis Research, University of Liverpool, UK

    Christian Setzkorn

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  1. Christian Setzkorn
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Editors and Affiliations

  1. Indian Statistical Institute, 203 B. T. Road, 700 108, Kolkata, India

    Ashish Ghosh

  2. F. M. University, 756 019, Balasore, India

    Satchidananda Dehuri

  3. Jadavpur University, 700 032, Kolkata, India

    Susmita Ghosh

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Setzkorn, C. (2008). Classification and Survival Analysis Using Multi-objective Evolutionary Algorithms. In: Ghosh, A., Dehuri, S., Ghosh, S. (eds) Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases. Studies in Computational Intelligence, vol 98. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77467-9_6

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  • DOI: https://doi.org/10.1007/978-3-540-77467-9_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-77466-2

  • Online ISBN: 978-3-540-77467-9

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