Differential Evolution for learning the classification method PROAFTN

  • Authors:

  • Feras Al-Obeidat;Nabil Belacel;Juan A. Carretero;Prabhat Mahanti

  • Affiliations:

  • Department of Computer Science, University of New Brunswick, Saint John, NB, Canada;Department of Computer Science, University of New Brunswick, Saint John, NB, Canada and Institute for Information Technology, National Research Council, Moncton, NB, Canada;Department of Mechanical Engineering, University of New Brunswick, Fredericton, NB, Canada;Department of Computer Science, University of New Brunswick, Saint John, NB, Canada

  • Venue:
  • Knowledge-Based Systems
  • Year:
  • 2010

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Abstract

This paper introduces a new learning technique for the multicriteria classification method PROAFTN. This new technique, called DEPRO, utilizes a Differential Evolution (DE) algorithm for learning and optimizing the output of the classification method PROAFTN. The limitation of the PROAFTN method is largely due to the set of parameters (e.g., intervals and weights) required to be obtained to perform the classification procedure. Therefore, a learning method is needed to induce and extract these parameters from data. DE is an efficient metaheuristic optimization algorithm based on a simple mathematical structure to mimic a complex process of evolution. Some of the advantages of DE over other global optimization methods are that it often converges faster and with more certainty than many other methods and it uses fewer control parameters. In this work, the DE algorithm is proposed to inductively obtain PROAFTN's parameters from data to achieve a high classification accuracy. Based on results generated from 12 public datasets, DEPRO provides excellent results, outperforming the most common classification algorithms.