Enhancing Discrimination Power with Genetic Feature Construction: A Grammatical Evolution Approach
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Miquilini:2016:CEC,
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author = "Patricia Miquilini and Rodrigo C. Barros and
Vinicius V {de Melo} and Marcio P. Basgalupp",
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title = "Enhancing Discrimination Power with Genetic Feature
Construction: A Grammatical Evolution Approach",
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booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary
Computation (CEC 2016)",
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year = "2016",
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editor = "Yew-Soon Ong",
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pages = "3824--3831",
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address = "Vancouver",
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month = "24-29 " # jul,
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming, Grammatical
Evolution",
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isbn13 = "978-1-5090-0623-6",
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DOI = "doi:10.1109/CEC.2016.7744274",
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abstract = "Data set preprocessing is a critical step for the
successful application of machine learning algorithms
in classification tasks. Even though we rely on
learning algorithms to pinpoint the optimal decision
boundaries in the feature space by properly detecting
latent relationships among the input features, their
performance is often bounded by the discriminative
power of the available features. Therefore, much effort
has been devoted to developing preprocessing methods
that are capable of transforming the input data with
the final goal of aiding the machine learning algorithm
in building high-quality classification models. One
such a method is feature construction, which is a
flexible preprocessing procedure that exploits linear
and nonlinear transformations of the original feature
space in an attempt to capture useful information that
is not explicit in the original data. Since the task of
feature construction can be modelled as a heuristic
search in the space of novel latent features, this
paper investigates an evolutionary approach for
performing such a task, namely grammatical evolution
(GE). In our proposed approach, GE is employed for
building an extra novel feature from the available
input data in order to maximize the predictive
performance of the learning algorithm in training data.
Results show that many interesting implicit
relationships are indeed found by the evolutionary
approach, improving the performance of two well known
decision-tree induction algorithms.",
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notes = "WCCI2016",
- }
Genetic Programming entries for
Patricia Miquilini
Rodrigo C Barros
Vinicius Veloso de Melo
Marcio Porto Basgalupp
Citations