An iterative genetic programming approach to prototype generation
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @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