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Learning Optimal Decision Lists as a Metaheuristic Search for Diagnosis of Parkinson’s Disease

  • Fernando de Carvalho GomesEmail author
  • José Gilvan Rodrigues Maia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10122)

Abstract

Decision Lists are a very general model representation. In learning decision structures from medical datasets one needs a simple understandable model. Such a model should correctly classify unknown cases. One must search for the most general decision structure using the training dataset as input, taking into account both complexity and goodness-of-fit of the underlying model. In this paper, we propose to search the Decision List state space as an optimization problem using a metaheuristic. We implemented the method and carried out experimentation over a well-known Parkinson’s Disease training set. Our results are encouraging when compared to other machine learning references on the same dataset.

Keywords

Stochastic search Learning decision lists Parkinson’s Disease classification 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Fernando de Carvalho Gomes
    • 1
    Email author
  • José Gilvan Rodrigues Maia
    • 1
  1. 1.Computer Science Department and Virtual University InstituteFederal University of CearáFortalezaBrazil

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