Dimensionality Reduction for Classification Using Divide-and-Conquer Based Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{wang:2024:CEC,
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author = "Peng Wang and Bing Xue and Jing Liang and
Mengjie Zhang",
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title = "Dimensionality Reduction for Classification Using
Divide-and-Conquer Based Genetic Programming",
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booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2024",
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editor = "Bing Xue",
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address = "Yokohama, Japan",
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month = "30 " # jun # " - 5 " # jul,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming,
Dimensionality reduction, Limiting, Accuracy,
Clustering algorithms, Evolutionary computation,
Feature extraction",
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isbn13 = "979-8-3503-0837-2",
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DOI = "doi:10.1109/CEC60901.2024.10612123",
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abstract = "Dimensionality reduction (DR) is to obtain meaningful
low-dimensional representation concealed within
high-dimensional data. Genetic programming (GP) has
been used to achieve DR for classification because of
the flexible representations and promising performance.
However, one drawback of many current GP-based DR
methods is the high computational demands, potentially
limiting their utility in high-dimensional scenarios.
To tackle this issue, this work develops a new GP
algorithm to achieve DR for classification. A
divide-and-conquer strategy based on feature clustering
is proposed to split the original feature set into
multiple small feature groups. Each tree in the
multi-tree GP is used to learn one high-level feature
from each feature group. The performance of the
proposed method is examined on 10 classification
datasets with varying difficulty. The results indicate
that the new GP method achieves significantly better
classification performance than commonly used feature
construction and feature selection methods. More
importantly, the proposed GP method uses fewer nodes
but achieves better or comparable classification
accuracy than the baseline multi-tree GP method.",
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notes = "also known as \cite{10612123}
WCCI 2024",
- }
Genetic Programming entries for
Peng Wang
Bing Xue
Jing Liang
Mengjie Zhang
Citations