Learning to Assemble Classifiers via Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8772

@Article{journals/ijprai/Acosta-MendozaMEA14,

• title = "Learning to Assemble Classifiers via Genetic Programming",
• author = "Niusvel Acosta-Mendoza and Alicia Morales-Reyes and Hugo Jair Escalante and Andres Gago Alonso",
• journal = "International Journal of Pattern Recognition and Artificial Intelligence",
• year = "2014",
• number = "7",
• volume = "28",
• keywords = "genetic algorithms, genetic programming, Pattern classification, heterogeneous ensembles",
• bibdate = "2014-10-20",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijprai/ijprai28.html#Acosta-MendozaMEA14",
• URL = "http://dx.doi.org/10.1142/S0218001414600052",
• DOI = "10.1142/S0218001414600052",
• abstract = "This paper introduces a novel approach for building heterogeneous ensembles based on genetic programming (GP). Ensemble learning is a paradigm that aims at combining individual classifier's outputs to improve their performance. Commonly, classifiers outputs are combined by a weighted sum or a voting strategy. However, linear fusion functions may not effectively exploit individual models redundancy and diversity. In this research, a GP-based approach to learn fusion functions that combine classifiers outputs is proposed. Heterogeneous ensembles are aimed in this study, these models use individual classifiers which are based on different principles (e.g. decision trees and similarity-based techniques). A detailed empirical assessment is carried out to validate the effectiveness of the proposed approach. Results show that the proposed method is successful at building very effective classification models, outperforming alternative ensemble methodologies. The proposed ensemble technique is also applied to fuse homogeneous models outputs with results also showing its effectiveness. Therefore, an in-depth analysis from different perspectives of the proposed strategy to build ensembles is presented with a strong experimental support.",
• notes = "IJPRAI",

}

Genetic Programming entries for Niusvel Acosta-Mendoza Alicia Morales-Reyes Hugo Jair Escalante Andres Gago Alonso

Citations