Evolutionary Design of Decision-Tree Algorithms Tailored to Microarray Gene Expression Data Sets
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- @Article{Barros:2014:ieeeTEC,
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author = "Rodrigo C. Barros and Marcio P. Basgalupp and
Alex A. Freitas and Andre C. P. L. F. {de Carvalho}",
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title = "Evolutionary Design of Decision-Tree Algorithms
Tailored to Microarray Gene Expression Data Sets",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2014",
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month = dec,
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volume = "18",
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number = "6",
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pages = "873--892",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "1089-778X",
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DOI = "doi:10.1109/TEVC.2013.2291813",
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size = "20 pages",
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abstract = "Decision-tree induction algorithms are widely used in
machine learning applications in which the goal is to
extract knowledge from data and present it in a
graphically intuitive way. The most successful strategy
for inducing decision trees is the greedy top-down
recursive approach, which has been continuously
improved by researchers over the past 40 years. In this
paper, we propose a paradigm shift in the research of
decision trees: instead of proposing a new manually
designed method for inducing decision trees, we propose
automatically designing decision-tree induction
algorithms tailored to a specific type of
classification data set (or application domain).
Following recent breakthroughs in the automatic design
of machine learning algorithms, we propose a
hyper-heuristic evolutionary algorithm called
hyper-heuristic evolutionary algorithm for designing
decision-tree algorithms (HEAD-DT) that evolves design
components of top-down decision-tree induction
algorithms. By the end of the evolution, we expect
HEAD-DT to generate a new and possibly better
decision-tree algorithm for a given application domain.
We perform extensive experiments in 35 real-world
microarray gene expression data sets to assess the
performance of HEAD-DT, and compare it with very well
known decision-tree algorithms such as C4.5, CART, and
REPTree. Results show that HEAD-DT is capable of
generating algorithms that significantly outperform the
baseline manually designed decision-tree algorithms
regarding predictive accuracy and F-measure.",
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notes = "also known as \cite{6670778}",
- }
Genetic Programming entries for
Rodrigo C Barros
Marcio Porto Basgalupp
Alex Alves Freitas
Andre Ponce de Leon F de Carvalho
Citations