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CFP Special Session on Evolutionary Multi-objective Machine Learning

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  • emoml.hais09
    Special Session on Evolutionary Multi-objective Machine Learning Description Many current research works have combined the global search abilities of
    Message 1 of 1 , Jan 2, 2009
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      Special Session on Evolutionary Multi-objective Machine Learning
      Description

      Many current research works have combined the global search abilities
      of Evolutionary Computation with Machine Learning algorithms. Most of
      these hybrid approaches use mono-objective fitness functions. However,
      many issues in Machine Learning are multi-objective in nature. For
      instance, in feature selection, the minimization of the number of
      attributes and the maximization of accuracy are conflicting goals.
      Also, new powerful multi-objective optimization algotithms have been
      developed. That is why recently, multi-objective approaches have been
      applied to Machine Learning problems such as: improving the
      generalization capabilities of learning algorithms, generating diverse
      classifiers for building ensembles, reducing the complexity of models
      for improving interpretability, multi-objective-based feature
      selection, clustering, etc. This special session welcomes articles on
      advances on evolutionary multi-objective-based Machine Learning.
      Papers comparing and studying the advantages and disadvantages of the
      multi-objective versus the mono-objective approach are also welcome.

      Topics include but are not limited to:

      * Evolutionary multi-objective techniques for improving the
      generalization capabilities of machine learning algorithms
      * Evolutionary multi-objective techniques for improving
      interpretability of models
      * Evolutionary multi-objective feature selection
      * Evolutionary multi-objective ensemble generation
      * Empirical and/or theoretical comparisons between evolutionary
      mono-objective and multi-objective machine learning techniques
      * Multi-objective Genetic Programming
      * New evolutionary multi-objective algorithms speciallized in
      machine learning
      * Applications of evolutionary multi-objective learning

      Review Process

      Papers will be reviewed by at least two members of the Program Committee
      Co-Chairs at EVANNAI, Computer Science Department, Universidad Carlos
      III de Madrid

      * Ricardo Aler
      * Inés M. Galván
      * José M. Valls

      Contact information

      * E-mail: aler@..., igalvan@..., jvalls@...
      * Postal Address: Avenida Universidad, 30; 28911 Leganés, Madrid
      (SPAIN)
      * Telephone: +34916249418
      * Fax: +34916249430
      * Url: http://eva.evannai.inf.uc3m.es/?q=node/223
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