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.
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Zafra, A., Ventura, S., Herrera-Viedma, E., Romero, C. (2007). Multiple Instance Learning with Genetic Programming for Web Mining. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_111
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DOI: https://doi.org/10.1007/978-3-540-73007-1_111
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