Paper Info
Title
A Comparison of Multi-objective Grammar-Guided Genetic Programming Methods to Multiple Instance Learning
Abstract
This paper develops a first comparative study of multi- objective algorithms in Multiple Instance Learning (MIL) applications. These algorithms use grammar-guided genetic programming, a robust classification paradigm which is able to generate understandable rules that are adapted to work with the MIL framework. The algorithms obtained are based on the most widely used and compared multi-objective evolutionary algorithms. Thus, we design and implement SPG3P-MI based on the Strength Pareto Evolutionary Algorithm, NSG3P-MI based on the Non-dominated Sorting Genetic Algorithm and MOGLG3P-MI based on the Multi-objective genetic local search. These approaches are tested with different MIL applications and compared to a previous single-objective grammar-guided genetic programming proposal. The results demonstrate the excellent performance of multi-objective approaches in achieving accurate models and their ability to generate comprehensive rules in the knowledgable discovery process.
Year
DOI
Venue
2009
10.1007/978-3-642-02319-4_54
HAIS
Keywords
Field
DocType
genetic algorithm,different mil application,multi-objective genetic local search,multi-objective approach,mil framework,multiple instance learning,multi-objective evolutionary algorithm,genetic programming,strength pareto evolutionary algorithm,multi-objective grammar-guided genetic programming,genetic programming proposal,genetics,local search
Computer science,Genetic programming,Genetic representation,Artificial intelligence,Cultural algorithm,Evolutionary programming,Population-based incremental learning,Evolutionary music,Quality control and genetic algorithms,Genetic algorithm,Machine learning
Conference
Volume
ISSN
Citations 
5572
0302-9743
0
PageRank 
References 
Authors
0.34
17
2
Name
Order
Citations
PageRank
Amelia Zafra143222.64
S. Ventura22318158.44