Evolving model trees for mining data sets with continuous-valued classes
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Potgieter20081513,
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author = "Gavin Potgieter and Andries P. Engelbrecht",
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title = "Evolving model trees for mining data sets with
continuous-valued classes",
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journal = "Expert Systems with Applications",
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volume = "35",
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number = "4",
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pages = "1513--1532",
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year = "2008",
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month = nov,
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keywords = "genetic algorithms, genetic programming, Data mining,
Continuous-valued classes, Model trees",
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ISSN = "0957-4174",
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broken = "http://www.sciencedirect.com/science/article/B6V03-4PMT2TF-1/2/2951ff5c090ff34723645688b51c34cd",
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DOI = "doi:10.1016/j.eswa.2007.08.060",
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abstract = "This paper presents a genetic programming (GP)
approach to extract symbolic rules from data sets with
continuous-valued classes, called GPMCC. The GPMCC
makes use of a genetic algorithm (GA) to evolve
multi-variate non-linear models [Potgieter, G., &
Engelbrecht, A. (2007). Genetic algorithms for the
structural optimisation of learned polynomial
expressions. Applied Mathematics and Computation] at
the terminal nodes of the GP. Several mechanisms have
been developed to optimise the GP, including a fragment
pool of candidate non-linear models, k-means clustering
of the training data to facilitate the use of
stratified sampling methods, and specialized mutation
and crossover operators to evolve structurally optimal
and accurate models. It is shown that the GPMCC is
insensitive to control parameter values. Experimental
results show that the accuracy of the GPMCC is
comparable to that of NeuroLinear and Cubist, while
producing significantly less rules with less complex
antecedents.",
- }
Genetic Programming entries for
Gavin Potgieter
Andries P Engelbrecht
Citations