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MODEL SELECTION VIA META-LEARNING: A COMPARATIVE STUDY
Journal article

MODEL SELECTION VIA META-LEARNING: A COMPARATIVE STUDY

  • ALEXANDROS, KALOUSIS Department of Computer Science, University of Geneva, 24, rue du General-Dufour, CH-1211 Geneve 4, Switzerland
  • MELANIE, HILARIO Department of Computer Science, University of Geneva, 24, rue du General-Dufour, CH-1211 Geneve 4, Switzerland
  • 2012-4-30
Published in:
  • International Journal on Artificial Intelligence Tools. - World Scientific Pub Co Pte Lt. - 2001, vol. 10, no. 04, p. 525-554
English The selection of an appropriate inducer is crucial for performing effective classification. In previous work we presented a system called NOEMON which relied on a mapping between dataset characteristics and inducer performance to propose inducers for specific datasets. Instance-based learning was used to create that mapping. Here we explore the use of decision trees inducers as the inducers on the meta-learning level. We believe that they posses a set of properties that match the properties of the meta-learning problem that we are trying to solve. The results show that the performance of the system is indeed improved with the use of the decision tree learners.
Language
  • English
Open access status
closed
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Persistent URL
https://sonar.ch/global/documents/279749
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