MetaSel 2014
Meta-learning and Algorithm Selection

Table of Contents

Preface

Invited Talk Abstracts

• Towards an Algorithm Selection Standard: Data Format and Tools 1 - 1
  Lars Kotthoff
  
• Bayesian Optimization for More Automatic Machine Learning 2 - 2
  Frank Hutter
  

Technical Papers

• Using Meta-Learning to Initialize Bayesian Optimization of Hyperparameters 3 - 10
  Matthias Feurer , Tobias Springenberg , Frank Hutter
  
• Similarity Measures of Algorithm Performance for Cost-Sensitive Scenarios 11 - 17
  Carlos Eduardo Castor de Melo , Ricardo Prudêncio
  
• Using Metalearning to Predict When Parameter Optimization Is Likely to Improve Classification Accuracy 18 - 23
  Parker Ridd , Christophe Giraud-Carrier
  
• Surrogate Benchmarks for Hyperparameter Optimization 24 - 31
  Katharina Eggensperger , Frank Hutter , Holger Hoos , Kevin Leyton-Brown
  
• A Framework To Decompose And Develop Metafeatures 32 - 36
  Fabio Pinto , Carlos Soares , Joao Mendes-Moreira
  
• Towards Meta-learning over Data Streams 37 - 38
  Jan van Rijn , Geoffrey Holmes , Bernhard Pfahringer , Joaquin Vanschoren
  
• Recommending Learning Algorithms and Their Associated Hyperparameters 39 - 40
  Michael Smith , Logan Mitchell , Christophe Giraud-Carrier , Tony Martinez
  
• An Easy to Use Repository for Comparing and Improving Machine Learning Algorithm Usage 41 - 48
  Michael Smith , Andrew White , Christophe Giraud-Carrier , Tony Martinez
  
• Measures for Combining Accuracy and Time for Meta-learning 49 - 50
  Salisu Abdulrahman , Pavel Brazdil
  

Abstracts

• Determining a Proper Initial Configuration of Red-Black Planning by Machine Learning 51 - 52
  Otakar Trunda , Roman Bartak
  
• Hybrid Multi-Agent System for Metalearning in Data Mining 53 - 54
  Klara Peskova , Jakub Smid , Martin Pilat , Ondrej Kazik , Roman Neruda
  
• Model Selection in Data Analysis Competitions 55 - 60
  David Kofoed Wind , Ole Winther