Multi-objective Genetic Programming for Multiple Instance Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{DBLP:conf/ecml/ZafraV07,

• author = "Amelia Zafra and Sebastian Ventura",
• title = "Multi-objective Genetic Programming for Multiple Instance Learning",
• booktitle = "18th European Conference on Machine Learning, ECML 2007",
• year = "2007",
• bibsource = "DBLP, http://dblp.uni-trier.de",
• editor = "Joost N. Kok and Jacek Koronacki and Ramon L{\'o}pez de M{\'a}ntaras and Stan Matwin and Dunja Mladenic and Andrzej Skowron",
• publisher = "Springer",
• series = "Lecture Notes in Computer Science",
• volume = "4701",
• pages = "790--797",
• address = "Warsaw, Poland",
• month = sep # " 17-21",
• keywords = "genetic algorithms, genetic programming, poster",
• isbn13 = "978-3-540-74957-8",
• DOI = "doi:10.1007/978-3-540-74958-5_81",
• size = "8 pages",
• 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.",
• notes = "http://www.ecmlpkdd2007.org/poster_E.html",

}

Genetic Programming entries for Amelia Zafra Gomez Sebastian Ventura

Citations