Dimensionality reduction in face detection: A genetic programming approach
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Neshatian:2009:IVCNZ,
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title = "Dimensionality reduction in face detection: A genetic
programming approach",
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author = "Kourosh Neshatian and Mengjie Zhang",
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year = "2009",
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pages = "391--396",
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booktitle = "Proceeding of the 24th International Conference Image
and Vision Computing New Zealand, IVCNZ '09",
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month = "23-25 " # nov,
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address = "Wellington",
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-4244-4697-1",
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ISSN = "2151-2205",
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DOI = "doi:10.1109/IVCNZ.2009.5378375",
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abstract = "The high number of features in many machine vision
applications has a major impact on the performance of
machine learning algorithms. Feature selection (FS) is
an avenue to dimensionality reduction. Evolutionary
search techniques have been very promising in finding
solutions in the exponentially growing search space of
FS problems. This paper proposes a genetic programming
(GP) approach to FS where the building blocks are
subsets of features and set operators. We use bit-mask
representation for subsets and a set of set operators
as primitive functions. The GP search, then combines
these subsets and set operations to find an optimal
subset of features. The task we study is a highly
imbalanced face detection problem. A modified version
of the Naive Bayes classification model is used as the
fitness function. Our results show that the proposed
algorithm can achieve a significant reduction in
dimensionality and processing time. Using the
GP-selected features, the performance of certain
classifiers can also be improved.",
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notes = "Also known as \cite{5378375}",
- }
Genetic Programming entries for
Kourosh Neshatian
Mengjie Zhang
Citations