Multiple instance learning with genetic programming for web mining

Authors:
A. Zafra;S. Ventura;E. Herrera-Viedma;C. Romero
Affiliations:
Department of Computer Science and Artificial Intelligence, University of Granada;Department of Computer Science and Numerical Analysis, University of Córdoba;Department of Computer Science and Artificial Intelligence, University of Granada;Department of Computer Science and Numerical Analysis, University of Córdoba
Venue:
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Year:
2007

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Abstract

The aim of this paper is to present a new tool of multiple instance learning which is designed using a grammar based genetic programming (GGP) algorithm. We study its application in Web Mining framework to identify web pages interesting for the users. This new tool called GGP-MI algorithm is evaluated and compared with other available algorithms which extend a well-known neighborhood based algorithm (k-nearest neighbour algorithm) to multiple instance learning. Computational experiments show that, the GGP-MI algorithm obtains competitive results, solves problems of other algorithms, such as sparsity and scalability and adds comprehensibility and clarity in the knowledge discovery process.