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 wellknown 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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Zafra, A., & Ventura, S. (2007). Multi-objective genetic programming for multiple instance learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4701 LNAI, pp. 790–797). Springer Verlag. https://doi.org/10.1007/978-3-540-74958-5_81
Mendeley helps you to discover research relevant for your work.