Directly Evolving Classifiers for Missing Data using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Tran:2016:CEC,
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author = "Cao Truong Tran and Mengjie Zhang and Peter Andreae",
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title = "Directly Evolving Classifiers for Missing Data using
Genetic Programming",
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booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary
Computation (CEC 2016)",
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year = "2016",
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editor = "Yew-Soon Ong",
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pages = "5278--5285",
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address = "Vancouver",
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month = "24-29 " # jul,
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-5090-0623-6",
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DOI = "doi:10.1109/CEC.2016.7748361",
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abstract = "Missing values are a common issue in many industrial
and real-world datasets. Coping with datasets
containing missing values is an important requirement
for classification because inadequate treatment of
missing values may result in large errors on
classification. Genetic programming (GP) has been
successfully used to evolve classifiers, but it has
been applied mainly to complete data. This paper
proposes IGP, a GP method for directly evolving
classifiers for missing data. In order to directly
evolve classifiers for missing data, IGP uses interval
functions as the GP function set and builds a set of
classifiers for each classification problem.
Experiments on 10 benchmark datasets compared IGP with
five other classifiers on classification performance.
Experimental results showed that, in most cases, IGP
achieves significantly better classification accuracy
than the other methods.",
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notes = "WCCI2016",
- }
Genetic Programming entries for
Cao Truong Tran
Mengjie Zhang
Peter Andreae
Citations