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A Probabilistic Iterative Local Search Algorithm Applied to Full Model Selection

  • Esteban Cortazar
  • Domingo Mery
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)

Abstract

Currently, there is no solution, which does not require a high runtime, to the problem of choosing preprocessing methods, feature selection algorithms and classifiers for a supervised learning problem. In this paper we present a method for efficiently finding a combination of algorithms and parameters that effectively describes a dataset. Furthermore, we present an optimization technique, based on ParamILS, which can be used in other contexts where each evaluation of the objective function is highly time consuming, but an estimate of this function is possible. In this paper, we present our algorithm and initial validation of it over real and synthetic data. In said validation, our proposal demonstrates a significant reduction in runtime, compared to ParamILS, while solving problems with these characteristics.

Keywords

Full Model Selection FMS Machine learning Challenge Iterative Local Search ILS 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Esteban Cortazar
    • 1
  • Domingo Mery
    • 1
  1. 1.Department of Computer Science, School of EngineeringPontificia Universidad Católica de ChileVicuña MackennaChile

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