Multiple Instance Learning with Genetic Programming for Web Mining

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.

• Type: Conference
• Year: 2007
• Where: IWANN
• Authors: Amelia Zafra, Sebastián Ventura, Enrique Herrera-Viedma, Cristóbal Romero
• Keywords: GGP-MI Algorithm, IWANN 2007, K-nearest Neighbour Algorithm, Multiple Instance Learning