Feature Construction, Feature Reduction and Search Space Reduction Using Genetic Programming
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- @InProceedings{Herrera-Sanchez:2022:ISCMI,
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author = "David Herrera-Sanchez and Efren Mezura-Montes and
Hector-Gabriel Acosta-Mesa",
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booktitle = "2022 9th International Conference on Soft Computing \&
Machine Intelligence (ISCMI)",
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title = "Feature Construction, Feature Reduction and Search
Space Reduction Using Genetic Programming",
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year = "2022",
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pages = "152--156",
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abstract = "Feature construction and feature selection are
essential pre-processing techniques in data mining,
especially for high-dimensional data. The principal
goals of such techniques are to increase accuracy in
classification tasks and reduce runtime in the learning
process. Genetic programming is used to construct a new
high-level feature space. Additionally, the feature
selection process, immersed in the task, is seized.
Therefore, a set of features with relevant information
is obtained. This paper presents an approach to
reducing the features of high-dimensional data
throughout genetic programming. Moreover, reducing the
search space eliminates features that do not have
considerable information over the generations of the
search process. Although the approach is simple,
competitive results are achieved. In the
implementation, the wrapper approach is used for the
classifier to lead the searching process.",
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keywords = "genetic algorithms, genetic programming, Runtime,
Feature extraction, Data mining, Task analysis, Machine
intelligence, feature reduction, feature construction,
high-dimensional data",
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DOI = "doi:10.1109/ISCMI56532.2022.10068452",
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ISSN = "2640-0146",
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month = nov,
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notes = "Also known as \cite{10068452}",
- }
Genetic Programming entries for
David Herrera-Sanchez
Efren Mezura-Montes
Hector-Gabriel Acosta-Mesa
Citations