Multiple Imputation for Missing Data Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Tran:2015:GECCO,

• author = "Cao Truong Tran and Mengjie Zhang and Peter Andreae",
• title = "Multiple Imputation for Missing Data Using Genetic Programming",
• booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation",
• year = "2015",
• 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",
• isbn13 = "978-1-4503-3472-3",
• pages = "583--590",
• keywords = "genetic algorithms, genetic programming, Evolutionary Machine Learning",
• month = "11-15 " # jul,
• organisation = "SIGEVO",
• address = "Madrid, Spain",
• URL = "http://doi.acm.org/10.1145/2739480.2754665",
• DOI = "doi:10.1145/2739480.2754665",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• 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.",
• 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