Feature Selection for GPSR Based on Maximal Information Coefficient and Shapley Values
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{rimas:2024:CEC,
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author = "Mohamad Rimas Mohamad Anfar and Qi Chen and
Mengjie Zhang",
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title = "Feature Selection for {GPSR} Based on Maximal
Information Coefficient and Shapley Values",
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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, Microwave
integrated circuits, Sociology, Focusing, Machine
learning, Feature extraction, Time measurement, Feature
importance, MIC, symbolic regression, Shapley value",
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isbn13 = "979-8-3503-0837-2",
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DOI = "doi:10.1109/CEC60901.2024.10611755",
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abstract = "Feature selection is a critical aspect of improving
the interpretability of machine learning models.
Genetic Programming (GP) has a built-in feature
selection mechanism that explores the search space to
include informative features in models. However, this
built-in mechanism is insufficient for identifying
important features, when dealing with high-dimensional
feature spaces. To overcome this limitation, the paper
introduces a novel feature importance measurement based
on the Maximal Infor-mation Coefficient and Shapley
Values. The proposed algorithm operates in two stages.
In the first stage, it identifies the best individuals
from different populations. In the second stage, the
best individuals from the first stage are used for the
calculation of the novel individual feature importance
measurement. The new feature importance measurement
offers valuable insights into the significance and
relevance of the selected features. Regression
experiments were conducted on six datasets to assess
the effectiveness of the proposed method. Furthermore,
comparisons were made with two other algorithms to
evaluate its performance. The results indicate that the
proposed approach enhances GP performance for high
dimensional datasets while maintaining GP trees of
similar size compared to standard GP.",
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notes = "also known as \cite{10611755}
WCCI 2024",
- }
Genetic Programming entries for
Mohamad Rimas Mohamad Anfar
Qi Chen
Mengjie Zhang
Citations