A Genetic Programming Approach to Hyper-Heuristic Feature Selection
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Hunt:2012:SEAL,
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author = "Rachel Hunt and Kourosh Neshatian and Mengjie Zhang",
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title = "A Genetic Programming Approach to Hyper-Heuristic
Feature Selection",
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booktitle = "The Ninth International Conference on Simulated
Evolution And Learning, SEAL 2012",
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year = "2012",
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editor = "Lam Thu Bui and Yew-Soon Ong and Nguyen Xuan Hoai and
Hisao Ishibuchi and Ponnuthurai Nagaratnam Suganthan",
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volume = "7673",
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series = "Lecture Notes in Computer Science",
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pages = "320--330",
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address = "Vietnam",
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month = dec # " 16-19",
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organisation = "Faculty of Information Technology, Le Quy Don
Technical University",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-642-34858-7",
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DOI = "doi:10.1007/978-3-642-34859-4_32",
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size = "11 pages",
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abstract = "Feature selection is the task of finding a subset of
original features which is as small as possible yet
still sufficiently describes the target concepts.
Feature selection has been approached through both
heuristic and meta-heuristic approaches.
Hyper-heuristics are search methods for choosing or
generating heuristics or components of heuristics, to
solve a range of optimisation problems. This paper
proposes a genetic-programming-based hyper-heuristic
approach to feature selection. The proposed method
evolves new heuristics using some basic components
(building blocks). The evolved heuristics act as new
search algorithms that can search the space of subsets
of features. The classification performance (accuracy)
of classifiers are improved by using small subsets of
features found by evolved heuristics.",
- }
Genetic Programming entries for
Rachel Hunt
Kourosh Neshatian
Mengjie Zhang
Citations