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Multi-objective Genetic Programming for Multiple Instance Learning

  • Amelia Zafra
  • Sebastián Ventura
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4701)

Abstract

This paper introduces the use of multi-objective evolutionary algorithms in multiple instance learning. In order to achieve this purpose, a multi-objective grammar-guided genetic programming algorithm (MOG3P-MI) has been designed. This algorithm has been evaluated and compared to other existing multiple instance learning algorithms. Research on the performance of our algorithm is carried out on two well-known drug activity prediction problems, Musk and Mutagenesis, both problems being considered typical benchmarks in multiple instance problems. Computational experiments indicate that the application of the MOG3P-MI algorithm improves accuracy and decreases computational cost with respect to other techniques.

Keywords

Multiple Instance Inductive Logic Programming Multiple Instance Learn Automatic Image Annotation Inductive Logic Programming System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Amelia Zafra
    • 1
  • Sebastián Ventura
    • 1
  1. 1.Department of Computer Science and Numerical Analysis, University of Córdoba 

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