Multiple Instance Learning with MultiObjective Genetic Programming for Web Mining

This paper introduces a multiobjective grammar based genetic programming algorithm to solve a Web Mining problem from multiple instance perspective. This algorithm, called MOG3P-MI, is evaluated and compared with other available algorithms which extend a well-known neighborhood based algorithm (k-nearest neighbour algorithm) and with a monoobjective version of grammar guided genetic programming G3P-MI. Computational experiments show that, the MOG3PMI algorithm obtains the best results, solves problems of knearest neighbour algorithms, such as sparsity and scalability, adds comprehensibility and clarity in the knowledge discovery process and overcomes the results of monoobjective version.

• Type: Conference
• Year: 2008
• Where: HIS
• Authors: Amelia Zafra, Eva Lucrecia Gibaja Galindo, Sebastián Ventura
• Keywords: Genetic Programming Algorithm, HIS 2008, Information Technology, K-nearest Neighbour Algorithm, Neighbour Algorithms