Multiple Imputation for Missing Data Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Tran:2015:GECCO,
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author = "Cao Truong Tran and Mengjie Zhang and Peter Andreae",
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title = "Multiple Imputation for Missing Data Using Genetic
Programming",
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booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference
on Genetic and Evolutionary Computation",
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year = "2015",
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editor = "Sara Silva and Anna I Esparcia-Alcazar and
Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and
Christine Zarges and Luis Correia and Terence Soule and
Mario Giacobini and Ryan Urbanowicz and
Youhei Akimoto and Tobias Glasmachers and
Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and
Marta Soto and Carlos Cotta and Francisco B. Pereira and
Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and
Heike Trautmann and Jean-Baptiste Mouret and
Sebastian Risi and Ernesto Costa and Oliver Schuetze and
Krzysztof Krawiec and Alberto Moraglio and
Julian F. Miller and Pawel Widera and Stefano Cagnoni and
JJ Merelo and Emma Hart and Leonardo Trujillo and
Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and
Carola Doerr",
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isbn13 = "978-1-4503-3472-3",
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pages = "583--590",
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keywords = "genetic algorithms, genetic programming, Evolutionary
Machine Learning",
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month = "11-15 " # jul,
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organisation = "SIGEVO",
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address = "Madrid, Spain",
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URL = "http://doi.acm.org/10.1145/2739480.2754665",
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DOI = "doi:10.1145/2739480.2754665",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "Missing values are a common problem in many real world
databases. Inadequate handing of missing data can lead
to serious problems in data analysis. A common way to
cope with this problem is to use imputation methods to
fill missing values with plausible values. This paper
proposes GPMI, a multiple imputation method that uses
genetic programming as a regression method to estimate
missing values. Experiments on eight datasets with six
levels of missing values compare GPMI with seven other
popular and advanced imputation methods on two
measures: the prediction accuracy and the
classification accuracy. The results show that, in most
cases, GPMI not only achieves better prediction
accuracy, but also better classification accuracy than
the other imputation methods.",
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notes = "Also known as \cite{2754665} GECCO-2015 A joint
meeting of the twenty fourth international conference
on genetic algorithms (ICGA-2015) and the twentith
annual genetic programming conference (GP-2015)",
- }
Genetic Programming entries for
Cao Truong Tran
Mengjie Zhang
Peter Andreae
Citations