A new imputation method based on genetic programming and weighted KNN for symbolic regression with incomplete data
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- @Article{DBLP:journals/soco/Al-Helali00021,
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author = "Baligh Al-Helali and Qi Chen and Bing Xue and
Mengjie Zhang",
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title = "A new imputation method based on genetic programming
and weighted {KNN} for symbolic regression with
incomplete data",
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journal = "Soft Computing",
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volume = "25",
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number = "8",
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pages = "5993--6012",
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year = "2021",
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month = apr,
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keywords = "genetic algorithms, genetic programming, Symbolic
regression, Incomplete data, KNN, Imputation",
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ISSN = "1432-7643",
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URL = "https://doi.org/10.1007/s00500-021-05590-y",
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DOI = "doi:10.1007/s00500-021-05590-y",
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timestamp = "Wed, 07 Apr 2021 01:00:00 +0200",
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biburl = "https://dblp.org/rec/journals/soco/Al-Helali00021.bib",
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bibsource = "dblp computer science bibliography, https://dblp.org",
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abstract = "Incompleteness is one of the problematic data quality
challenges in real-world machine learning tasks. A
large number of studies have been conducted for
addressing this challenge. However, most of the
existing studies focus on the classification task and
only a limited number of studies for symbolic
regression with missing values exist. a new imputation
method for symbolic regression with incomplete data is
proposed. The method aims to improve both the
effectiveness and efficiency of imputing missing values
for symbolic regression. This method is based on
genetic programming (GP) and weighted K-nearest
neighbors (KNN). It constructs GP-based models using
other available features to predict the missing values
of incomplete features. The instances used for
constructing such models are selected using weighted
KNN. The experimental results on real-world data sets
show that the proposed method outperforms a number of
state-of-the-art methods with respect to the imputation
accuracy, the symbolic regression performance, and the
imputation time.",
- }
Genetic Programming entries for
Baligh Al-Helali
Qi Chen
Bing Xue
Mengjie Zhang
Citations