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Exploiting Propositionalization Based on Random Relational Rules for Semi-supervised Learning

  • Grant Anderson
  • Bernhard Pfahringer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5012)

Abstract

In this paper we investigate an approach to semi-supervised learning based on randomized propositionalization, which allows for applying standard propositional classification algorithms like support vector machines to multi-relational data. Randomization based on random relational rules can work both with and without a class attribute and can therefore be applied simultaneously to both the labeled and the unlabeled portion of the data present in semi-supervised learning.

An empirical investigation compares semi-supervised propositionalization to standard propositionalization using just the labeled data portion, as well as to a variant that also just uses the labeled data portion but includes the label information in an attempt to improve the resulting propositionalization. Preliminary experimental results indicate that propositionalization generated on the full dataset, i.e. the semi- supervised approach, tends to outperform the other two more standard approaches.

Keywords

semi-supervised propositionalization randomization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Grant Anderson
    • 1
  • Bernhard Pfahringer
    • 1
  1. 1.Department of Computer ScienceUniversity of WaikatoHamiltonNew Zealand

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