Meta-data: Characterization of Input Features for Meta-learning

  • Ciro Castiello
  • Giovanna Castellano
  • Anna Maria Fanelli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3558)


Common inductive learning strategies offer the tools for knowledge acquisition, but possess some inherent limitations due to the use of fixed bias during the learning process. To overcome limitations of such base-learning approaches, a novel research trend is oriented to explore the potentialities of meta-learning, which is oriented to the development of mechanisms based on a dynamical search of bias. This could lead to an improvement of the base-learner performance on specific learning tasks, by profiting of the accumulated past experience. As a significant set of I/O data is needed for efficient base-learning, appropriate meta-data characterization is of crucial importance for useful meta-learning. In order to characterize meta-data, firstly a collection of meta-features discriminating among different base-level tasks should be identified. This paper focuses on the characterization of meta-data, through an analysis of meta-features that can capture the properties of specific tasks to be solved at base level. This kind of approach represents a first step toward the development of a meta-learning system, capable of suggesting the proper bias for base-learning different specific task domains.


Support Vector Regression Entire Dataset Numerical Attribute Average Mutual Information Input Attribute 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ciro Castiello
    • 1
  • Giovanna Castellano
    • 1
  • Anna Maria Fanelli
    • 1
  1. 1.CILab – Computational Intelligence Laboratory, Computer Science DepartmentUniversity of BariBariItaly

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