Evolutionary model trees for handling continuous classes in machine learning
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- @Article{Barros2011954,
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author = "Rodrigo C. Barros and Duncan D. Ruiz and
Marcio P. Basgalupp",
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title = "Evolutionary model trees for handling continuous
classes in machine learning",
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journal = "Information Sciences",
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year = "2011",
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volume = "181",
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number = "5",
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pages = "954--971",
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keywords = "genetic algorithms, genetic programming, Evolutionary
algorithms, Model trees, Continuous classes, Machine
learning",
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ISSN = "0020-0255",
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URL = "http://www.sciencedirect.com/science/article/B6V0C-51GHWYC-1/2/2ba74d92cb03abc637a4c377b47a4dbe",
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DOI = "doi:10.1016/j.ins.2010.11.010",
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size = "18 pages",
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abstract = "Model trees are a particular case of decision trees
employed to solve regression problems. They have the
advantage of presenting an interpretable output,
helping the end-user to get more confidence in the
prediction and providing the basis for the end-user to
have new insight about the data, confirming or
rejecting hypotheses previously formed. Moreover, model
trees present an acceptable level of predictive
performance in comparison to most techniques used for
solving regression problems. Since generating the
optimal model tree is an NP-Complete problem,
traditional model tree induction algorithms make use of
a greedy top-down divide-and-conquer strategy, which
may not converge to the global optimal solution. we
propose a novel algorithm based on the use of the
evolutionary algorithms paradigm as an alternate
heuristic to generate model trees in order to improve
the convergence to globally near-optimal solutions. We
call our new approach evolutionary model tree induction
(E-Motion). We test its predictive performance using
public UCI data sets, and we compare the results to
traditional greedy regression/model trees induction
algorithms, as well as to other evolutionary
approaches. Results show that our method presents a
good trade-off between predictive performance and model
comprehensibility, which may be crucial in many machine
learning applications.",
- }
Genetic Programming entries for
Rodrigo C Barros
Duncan Dubugras Ruiz
Marcio Porto Basgalupp
Citations