An iterative genetic programming approach to prototype generation

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Valencia-Ramirez:2016:GPEM,

• author = "Jose Maria Valencia-Ramirez and Mario Graff and Hugo Jair Escalante and Jaime Cerda-Jacobo",
• title = "An iterative genetic programming approach to prototype generation",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2017",
• volume = "18",
• number = "2",
• pages = "123--147",
• month = jun,
• keywords = "genetic algorithms, genetic programming, K-nearest neighbours, Prototype generation, Pattern classification",
• ISSN = "1389-2576",
• DOI = "doi:10.1007/s10710-016-9279-3",
• size = "25 pages",
• abstract = "In this paper, we propose a genetic programming (GP) approach to the problem of prototype generation for nearest-neighbour (NN) based classification. The problem consists of learning a set of artificial instances that effectively represents the training set of a classification problem, with the goal of reducing the storage requirements and the computational cost inherent in KNN classifiers. This work introduces an iterative GP technique to learn such artificial instances based on a non-linear combination of instances available in the training set. Experiments are reported in a benchmark for prototype generation. Experimental results show our approach is very competitive with the state of the art, in terms of accuracy and in its ability to reduce the training set size.",

}

Genetic Programming entries for Jose Maria Valencia-Ramirez Mario Graff Guerrero Hugo Jair Escalante Jamie Cerda

Citations