- author = "Amelia {Zafra Gomez}",
- title = "Guided Genetic Programming Models for Multiple Instance Learning",
- titulo = "Modelos de Programacion Genetica Gramatical para Aprendizaje con Multiples Instancias",
- school = "University of Granada",
- year = "2009",
- address = "Spain",
- month = jul,
- keywords = "genetic algorithms, genetic programming",
-
URL = "
https://www.uco.es/kdis/research/theses/thesis-azafra/",
-
URL = "
http://www.uco.es/grupos/kdis/docs/thesis/2009-AZafra.pdf",
- broken = "http://www.uco.es/grupos/kdis/index.php?option=com_jresearch&view=thesis&task=show&id=2&Itemid=51&lang=en",
- size = "337 pages",
-
abstract = "This work focuses on the design of grammatical genetic
programming models for solving different paradigm of
learning applications with multiple instances.
First, we review the status of art of this learning. Following this review, we find that almost all learning paradigms used in machine learning have been extended to this paradigm, but there are no proposals of Evolutionary Algorithms (EAs) in this learning framework. EAs are a good alternative in different learning paradigms which have been applied, the large number of publications appeared since its appearance is an evidence of this popularity. In this work grammatical genetic programming methods both mono-and multi-objective are introduced for the resolution of different applications. In first place, an experimental study using benchmark data sets is carried out to demonstrate their effectiveness with respect to the most relevant proposals done over the years. Then, the models are applied over two real problems: web index page recommendation and prediction of a student's academic performance considering the work developed in the educational platform; these problems approached from a traditional supervised learning contain many missing values making difficult the correct classification. Using MIL, we seek a more flexible representation to solve them.",
-
notes = "in Spanish
Supervisor: Sebastian Ventura Soto",
