Call for participation

Machine Learning Journal

2nd Announcement:

Special Issue On Meta-Learning

C. Giraud-Carrier, R. Vilalta, and P. Brazdil, Guest Editors

MOTIVATION AND RESEARCH ISSUES

The proliferation of algorithms in Machine Learning (ML) and the growing interest in Data Mining (DM) have created a need for techniques and tools that facilitate the use of ML by novice users (e.g., to select adequate algorithms for specific business problems). Such tools will facilitate the transition of ML from research labs into industry. Discovering new algorithms (or versions thereof) has occupied much of the research of the past decade with reasonable success. Despite empirical studies comparing various algorithms, however, much remains to be learned about what makes a particular algorithm work well (or not) in a particular domain. There is a need to formulate or acquire such meta-knowledge, and make consistent use of it.

Although the term meta-learning has been ascribed different meanings by different researchers, for the purpose of this special issue, meta-learning is defined as any attempt to learn from the learning process itself. The goal is to understand how learning itself can become flexible and/or adaptable, taking into account the domain or task under study. Papers are solicited on the following subjects:
 

  •  EXPLOITING META-LEARNING
  • Selection of ML/DM algorithms from a given set
  • Selection of parameterized versions of a particular ML/DM algorithm
  • Selection of pre-/post-processing tasks for a given set of ML/DM algorithms
  • Flexible/adaptable combination of ML/DM algorithms (e.g., combinations of classifiers)
  • Flexible/adaptable design of complex system from basic parts (e.g., combination of pre-processing tasks and DM steps)
  • Automatic shift of bias
  •   FOUNDATION OF META-LEARNING
  • Evaluation and comparison of meta-learners, including multi-criteria (i.e., beyond predictive accuracy)
  • Theoretical studies of algorithms' performance (including studies focused on particular components of algorithms) and their impact on meta-learning
  • Empirical studies of algorithms' performance (including studies focused on particular components of algorithms) and their contribution to meta-learning
  • Methods for dealing with small amounts of meta-data (including planning systematic experiments and ways to reduce meta-data acquisition costs) and their utility for meta-learning
  •  ISSUES WITH META-DATA
  • Crafting domain/task characteristics and their relation to learning performance
  • Theoretical and/or empirical studies reporting on domain/task characteristics that are relevant or not to the meta-learning process

    • SUBMISSION INFORMATION:

    Manuscripts should be prepared for 8 1/2 x 11 in paper, with pages numbered consecutively. Papers should be at most 20,000 words in length, with  full-page figures counting for 400 words.  Detailed guidelines are at:
     http://www.cs.ualberta.ca/~holte/mlj/initialsubmission.pdf

     We kindly ask that all submissions be made electronically, as a postscript or pdf attachment to: jml@wkap.com.
     Since this is a special issue, please be sure also to:

  • clearly state, in the body of your email, that your submission is for this special issue on meta-learning.
  • cc your submission to: cgc@elca.ch, for double-checking.


    • If you cannot submit your paper electronically, please send five (5) hardcopies of the manuscript to Kluwer at the address specified in the  instructions, and one (1) hardcopy to:
     Christophe Giraud-Carrier
     ELCA Informatique SA   Av. de la Harpe, 22-24
     CH-1000 Lausanne 13  Switzerland
     Tel: +41-21-6132111  Fax: +41-21-6134700  e-mail: cgc@elca.ch

     Be sure your cover letter clearly identifies your submission as being made to the special issue on meta-learning.
     All queries regarding this special issue should be directed to Christophe Giraud-Carrier (cgc@elca.ch).

     The NEW SUBMISSION DEADLINE is SEPTEMBER 20, 2002.
     Submissions must not have appeared in, nor be under consideration by, other journals.

    REVIEWING PROCESS
    The submissions will undergo the usual reviewing process.