Unsupervised Elimination of Redundant Features Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{DBLP:conf/ausai/NeshatianZ09,
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author = "Kourosh Neshatian and Mengjie Zhang",
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title = "Unsupervised Elimination of Redundant Features Using
Genetic Programming",
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booktitle = "Proceedings of the 22nd Australasian Joint Conference
on Artificial Intelligence (AI'09)",
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year = "2009",
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editor = "Ann E. Nicholson and Xiaodong Li",
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volume = "5866",
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series = "Lecture Notes in Computer Science",
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pages = "432--442",
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bibsource = "DBLP, http://dblp.uni-trier.de",
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address = "Melbourne, Australia",
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month = dec # " 1-4",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-642-10438-1",
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DOI = "doi:10.1007/978-3-642-10439-8_44",
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abstract = "While most feature selection algorithms focus on
finding relevant features, few take the redundancy
issue into account. We propose a nonlinear redundancy
measure which uses genetic programming to find the
redundancy quotient of a feature with respect to a
subset of features. The proposed measure is
unsupervised and works with unlabeled data. We
introduce a forward selection algorithm which can be
used along with the proposed measure to perform feature
selection over the output of a feature ranking
algorithm. The effectiveness of the proposed method is
assessed by applying it to the output of the Chi-square
feature ranker on a classification task. The results
show significant improvements in the performance of
decision tree and SVM classifiers.",
- }
Genetic Programming entries for
Kourosh Neshatian
Mengjie Zhang
Citations