Genetic Programming of Prototypes for Pattern Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{conf/ibpria/EscalanteMGM13,

• author = "Hugo Jair Escalante and Karlo Mendoza and Mario Graff and Alicia Morales-Reyes",
• title = "Genetic Programming of Prototypes for Pattern Classification",
• booktitle = "Proceedings of the 6th Iberian Conference on Pattern Recognition and Image Analysis, {IbPRIA 2013}",
• year = "2013",
• editor = "Joao M. Sanches and Luisa Mico and Jaime S. Cardoso",
• volume = "7887",
• series = "Lecture Notes in Computer Science",
• pages = "100--107",
• address = "Funchal, Madeira, Portugal",
• month = jun # " 5-7",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• bibdate = "2013-05-28",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ibpria/ibpria2013.html#EscalanteMGM13",
• isbn13 = "978-3-642-38627-5",
• URL = "http://dx.doi.org/10.1007/978-3-642-38628-2",
• DOI = "doi:10.1007/978-3-642-38628-2_11",
• size = "8 pages",
• abstract = "This paper introduces a genetic programming approach to the generation of classification prototypes. Prototype-based classification is a pattern recognition methodology in which the training set of a classification problem is represented by a small subset of instances. The assignment of labels to test instances is usually done by a 1NN rule. We propose a new prototype generation method, based on genetic programming, in which examples of each class are automatically combined to generate highly effective classification prototypes. The genetic program aims to maximise an estimate of the generalisation performance of a 1NN classifier using the prototypes. We report experimental results on a benchmark for the evaluation of prototype generation methods. Experimental results show the validity of our approach: the proposed method outperforms most of the state of the art techniques when using both small and large data sets. Better results are obtained for data sets with numeric attributes only, although the performance of our method on mixed data is very competitive as well.",

}

Genetic Programming entries for Hugo Jair Escalante Karlo Mario Mendoza Mendoza Mario Graff Guerrero Alicia Morales-Reyes

Citations