Multi-Objective Semi-Supervised Feature Selection and Model Selection Based on Pearson’s Correlation Coefficient

  • Frederico Coelho
  • Antonio Padua Braga
  • Michel Verleysen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6419)

Abstract

This paper presents a Semi-Supervised Feature Selection Method based on a univariate relevance measure applied to a multiobjective approach of the problem. Along the process of decision of the optimal solution within Pareto-optimal set, atempting to maximize the relevance indexes of each feature, it is possible to determine a minimum set of relevant features and, at the same time, to determine the optimal model of the neural network.

Keywords

Semi-supervised feature selection Pearson Relief 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Frederico Coelho
    • 1
  • Antonio Padua Braga
    • 1
  • Michel Verleysen
    • 2
  1. 1.Universidade Federal de Minas GeraisBelo HorizonteBrazil
  2. 2.Universite Catholique de LouvainLouvain-la-NeuveBelgium

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