A gene expression programming approach for evolving multi-class image classifiers
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- @InProceedings{conf/lacci/AquinoRGBL17,
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author = "Nelson Marcelo Romero Aquino and Manasses Ribeiro and
Matheus Gutoski and Cesar Manuel {Vargas Benitez} and
Heitor Silverio Lopes",
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title = "A gene expression programming approach for evolving
multi-class image classifiers",
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booktitle = "2017 IEEE Latin American Conference on Computational
Intelligence (LA-CCI)",
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year = "2017",
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, gene
expression programming",
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isbn13 = "978-1-5386-3734-0",
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URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8275062",
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DOI = "doi:10.1109/LA-CCI.2017.8285696",
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size = "6 pages",
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abstract = "This paper presents a methodology to perform
multi-class image classification using Gene Expression
Programming(GEP) in both balanced and unbalanced
datasets. Descriptors are extracted from images and
then their dimensionality are reduced by applying
Principal Component Analysis. The aspects extracted
from images are texture, colour and shape that are,
later, concatenated in a feature vector. Finally, GEP
is used to evolve trees capable of performing as
classifiers using the features as terminals. The
quality of the solution evolved is evaluated by the
introduced Cross-Entropy-Loss-based fitness function
and compared with standard fitness function (both
accuracy and product of sensibility and specificity). A
novel GEP function linker Softmax-based is introduced.
GEP performance is compared with the obtained by
classifiers with tree structure, as C4.5 and Random
Forest algorithms. Results show that GEP is capable of
evolving classifiers able to achieve satisfactory
results for image multi-class classification.",
-
notes = "Also known as \cite{8285696}",
- }
Genetic Programming entries for
Nelson Marcelo Romero Aquino
Manasses Ribeiro
Matheus Gutoski
Cesar Manuel Vargas Benitez
Heitor Silverio Lopes
Citations