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Characterization of Learning Instances for Evolutionary Meta-Learning

Abstract : Machine learning has proven to be a powerful tool in diverse fields, and is getting more and more widely used by non-experts. One of the foremost difficulties they encounter lies in the choice and calibration of the machine learning algorithm to use. Our objective is thus to provide assistance in the matter, using a meta-learning approach based on an evolutionary heuristic. We expand here previous work presenting the intended workflow of a modeling assistant by describing the characterization of learning instances we intend to use.
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Submitted on : Friday, September 22, 2017 - 2:46:43 PM
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  • HAL Id : hal-01592040, version 1
  • OATAO : 16866


William Raynaut, Chantal Soulé-Dupuy, Nathalie Vallès-Parlangeau, Cédric Dray, Philippe P. Valet. Characterization of Learning Instances for Evolutionary Meta-Learning. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2015), Sep 2015, Porto, Portugal. pp. 198-205. ⟨hal-01592040⟩



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