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Evolutionary Multi-Objective Approach for Prototype Generation and Feature Selection

  • Alejandro Rosales-Pérez
  • Jesus A. Gonzalez
  • Carlos A. Coello-Coello
  • Carlos A. Reyes-Garcia
  • Hugo Jair Escalante
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8827)

Abstract

This paper introduces EMOPG+FS, a novel approach to prototype generation and feature selection that explicitly minimizes the classification error rate, the number of prototypes, and the number of features. Under EMOPG+FS, prototypes are initialized from a subset of training instances, whose positions are adjusted through a multi-objective evolutionary algorithm. The optimization process aims to find a set of suitable solutions that represent the best possible trade-offs among the considered criteria. Besides this, we also propose a strategy for selecting a single solution from the several that are generated during the multi-objective optimization process.We assess the performance of our proposed EMOPG+FS using a suite of benchmark data sets and we compare its results with respect to those obtained by other evolutionary and non-evolutionary techniques. Our experimental results indicate that our proposed approach is able to achieve highly competitive results.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alejandro Rosales-Pérez
    • 1
  • Jesus A. Gonzalez
    • 1
  • Carlos A. Coello-Coello
    • 2
  • Carlos A. Reyes-Garcia
    • 1
  • Hugo Jair Escalante
    • 1
  1. 1.Computer Science Department, TonantzintlaInstituto Nacional de Astrofísica, Óptica y Electrónica (INAOE)PueblaMexico
  2. 2.Computer Science DepartmentCINVESTAV-IPNMexico CityMexico

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