Genetic Programming of Heterogeneous Ensembles for Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{conf/ciarp/EscalanteAMA13,

• author = "Hugo Jair Escalante and Niusvel Acosta-Mendoza and Alicia Morales-Reyes and Andres Gago Alonso",
• title = "Genetic Programming of Heterogeneous Ensembles for Classification",
• year = "2013",
• booktitle = "Proceedings of the 18th Iberoamerican Congress on Image Analysis, Computer Vision, and Applications (CIARP 2013) Part {I}",
• editor = "Jose Ruiz-Shulcloper and Gabriella Sanniti di Baja",
• volume = "8258",
• series = "Lecture Notes in Computer Science",
• pages = "9--16",
• address = "Havana, Cuba",
• month = nov # " 20-23",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• bibdate = "2013-11-17",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ciarp/ciarp2013-1.html#EscalanteAMA13",
• isbn13 = "978-3-642-41821-1",
• URL = "http://dx.doi.org/10.1007/978-3-642-41822-8",
• size = "8 pages",
• abstract = "The ensemble classification paradigm is an effective way to improve the performance and stability of individual predictors. Many ways to build ensembles have been proposed so far, most notably bagging and boosting based techniques. Evolutionary algorithms (EAs) also have been widely used to generate ensembles. In the context of heterogeneous ensembles EAs have been successfully used to adjust weights of base classifiers or to select ensemble members. Usually, a weighted sum is used for combining classifiers outputs in both classical and evolutionary approaches. This study proposes a novel genetic program that learns a fusion function for combining heterogeneous-classifiers outputs. It evolves a population of fusion functions in order to maximise the classification accuracy. Highly non-linear functions are obtained with the proposed method, subsuming the existing weighted-sum formulations. Experimental results show the effectiveness of the proposed approach, which can be used not only with heterogeneous classifiers but also with homogeneous-classifiers and under bagging/boosting based formulations.",

}

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

Citations