A Genetic Programming-based Wrapper Imputation Method for Symbolic Regression with Incomplete Data
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Al-Helali:2019:SSCI,
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author = "Baligh Al-Helali and Qi Chen and Bing Xue and
Mengjie Zhang",
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booktitle = "2019 IEEE Symposium Series on Computational
Intelligence (SSCI)",
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title = "A Genetic Programming-based Wrapper Imputation Method
for Symbolic Regression with Incomplete Data",
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year = "2019",
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pages = "2395--2402",
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month = dec,
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/SSCI44817.2019.9002861",
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abstract = "Dealing with missing values is one of the challenges
in symbolic regression on many real-world data sets.
One of the popular approaches to address this challenge
is to use imputation. Traditional imputation methods
are usually performed based on the predictive features
without considering the original target variable. In
this work, a genetic programming-based wrapper
imputation method is proposed, which wrappers a
regression method to consider the target variable when
constructing imputation models for the incomplete
features. In addition to the imputation performance,
the regression performance is considered for evaluating
the imputation models. Genetic programming (GP) is used
for building the imputation models and decision tree
(DT) is used for evaluating the regression performance
during the GP evolutionary process. The experimental
results show that the proposed method has a significant
advance in enhancing the symbolic regression
performance compared with some state-of- the-art
imputation methods.",
-
notes = "Also known as \cite{9002861}",
- }
Genetic Programming entries for
Baligh Al-Helali
Qi Chen
Bing Xue
Mengjie Zhang
Citations