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HAIS 2012
International Conference on Hybrid Artificial Intelligence Systems
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Methods of Classifier Fusion

  • Combining classifiers are very attractive and effective approach for decision-making. The main goal of session is to discuss the new theoretical trends and the applications of multiple classifiers concept, information fusion and related approaches. Papers describing original work in the following and related research topics are welcome:
    1. Theoretical foundations of multiple classifiers
    2. Methods for classifier fusion
    3. Ensemble design
    4. Methods of decision making based on the information from different sources
    5. Methods for classifier selection
    6. Methods of improving qualities of weak classifiers (boosting, bagging, etc.)
    7. Method of measuring and ensuring diversity in classifier ensembles
    8. Multiple classifier system design
    9. Designing efficient computational systems for multiple classifiers
    10. Applications (especially from medical area but not only limited to)
  • Co-Chairs
    •
    1. Bruno Baruque, Universidad de Burgos, Spain
    2. Emilio Corchado, Universidad de Salamanca, Spain

  • Contact information
    •
    Dr. Bruno Baruque
    E-mail: bbaruque@ubu.es
  • Program Committee
    •
    1. Emilio Corchado, University of Burgos, Spain
    2. Álvaro Herrero, Universidad de Burgos, Spain