Simultaneous generation of prototypes and features through genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8620

@InProceedings{Garcia-Limon:2014:GECCO,

• author = "Mauricio Garcia-Limon and Hugo Jair Escalante and Eduardo Morales and Alicia Morales-Reyes",
• title = "Simultaneous generation of prototypes and features through genetic programming",
• booktitle = "GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation",
• year = "2014",
• editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges",
• isbn13 = "978-1-4503-2662-9",
• pages = "517--524",
• keywords = "genetic algorithms, genetic programming",
• month = "12-16 " # jul,
• organisation = "SIGEVO",
• address = "Vancouver, BC, Canada",
• URL = "http://doi.acm.org/10.1145/2576768.2598356",
• DOI = "10.1145/2576768.2598356",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "Nearest-neighbour (NN) methods are highly effective and widely used pattern classification techniques. There are, however, some issues that hinder their application for large scale and noisy data sets; including, its high storage requirements, its sensitivity to noisy instances, and the fact that test cases must be compared to all of the training instances. Prototype (PG) and feature generation (FG) techniques aim at alleviating these issues to some extent; where, traditionally, both techniques have been implemented separately. This paper introduces a genetic programming approach to tackle the simultaneous generation of prototypes and features to be used for classification with a NN classifier. The proposed method learns to combine instances and attributes to produce a set of prototypes and a new feature space for each class of the classification problem via genetic programming. An heterogeneous representation is proposed together with ad-hoc genetic operators. The proposed approach overcomes some limitations of NN without degradation in its classification performance. Experimental results are reported and compared with several other techniques. The empirical assessment provides evidence of the effectiveness of the proposed approach in terms of classification accuracy and instance/feature reduction.",
• notes = "Also known as \cite{2598356} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)",

}

Genetic Programming entries for Mauricio Garcia-Limon Hugo Jair Escalante Eduardo F Morales Alicia Morales-Reyes

Citations