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Special Issue on Multi-objective Evolutionary Feature Selection

To be published in the journal Information (http://www.mdpi.com/journal/information/, ISSN 2078-2489)

In recent years, it has been shown that Multi-objective Evolutionary Algorithms are powerful techniques to solve feature selection problems. The success lies fundamentally in the suitability of the Multi-objective Evolutionary Algorithm’s ability to approximate solutions in NP-hard problems, as well as in the possibility of addressing the feature selection problem as a multi-objective optimization problem where performance is maximized and the number of selected attributes is minimized, thus reducing the complexity of the models while improving their performance.

This Special Issue invites original research papers that report on the state-of-the-art and recent advancements in Multi-objective Evolutionary Computation techniques for Feature Selection. The scope of this Special Issue encompasses applications in Engineering, Artificial Intelligence, Physical Science, Social Science, Business, Economy, Market Research, and Medical and Health Care. Topics of interest include (but are not limited to) the following subject categories:

  • Multi-objective evolutionary univariate/multivariate feature selection methods for classification/regression/clustering/association rules.
  • Multi-objective evolutionary filter/wrapper/embedded feature selection methods for classification/regression/clustering/association rules.
  • Multi-objective evolutionary feature selection for unbalanced data.
  • Multi-objective evolutionary feature selection for multiple instance learning.
  • Multi-objective evolutionary feature selection for multi-class classification.
  • Multi-objective evolutionary feature selection for fuzzy classification.
  • Multi-objective evolutionary feature selection for text classification.
  • Multi-objective evolutionary feature selection for time-series forecasting.
  • New representations and variation operators for multi-objective evolutionary feature selection.
  • Multi-objective evolutionary feature selection with many objectives.
  • Multi-objective differential evolution feature selection.
  • Decision making in multi-objective evolutionary feature selection.
  • New performance metrics for multi-objective evolutionary feature selection.
  • Multi-objective evolutionary instance/feature selection.
  • Multi-objective evolutionary feature selection for big data.
  • Parallel multi-objective evolutionary feature selection.
  • Distributed multi-objective evolutionary feature selection.

For more details please visit the special issue website: http://www.mdpi.com/journal/information/special_issues/Feature_Selection

The manuscript submission deadline is 1 April 2019. You could send your manuscript earlier or up until the deadline. Papers will be process soon once received.

For further details on the submission process, please see the instructions for authors at the journal website: http://www.mdpi.com/journal/information/instructions

Guest Editors

Prof. Fernando Jiménez 
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Affiliation: Faculty of Informatics, Department of Information and Communications Engineering, University of Murcia, Murcia, Spain
Homepage Linkhttp://webs.um.es/fernan
Research Interests: evolutionary computation; multi-objective constrained optimization; machine learning; feature selection; fuzzy classification; intelligent data analysis; big data

Co-Guest Editor

Prof. José T. Palma
Email: Esta dirección de correo electrónico está siendo protegida contra los robots de spam. Necesita tener JavaScript habilitado para poder verlo.
Affiliation: Faculty of Informatics, Department of Information and Communications Engineering, University of Murcia, Murcia, Spain
Research Interests: intelligent data analysis; knowledge-based; ambient intelligent applications; fuzzy logic; ontologies; temporal reasoning

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