A Genetic Programming Approach to Hyper-Heuristic Feature Selection

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

@InProceedings{Hunt:2012:SEAL,

• author = "Rachel Hunt and Kourosh Neshatian and Mengjie Zhang",
• title = "A Genetic Programming Approach to Hyper-Heuristic Feature Selection",
• booktitle = "The Ninth International Conference on Simulated Evolution And Learning, SEAL 2012",
• year = "2012",
• editor = "Lam Thu Bui and Yew-Soon Ong and Nguyen Xuan Hoai and Hisao Ishibuchi and Ponnuthurai Nagaratnam Suganthan",
• volume = "7673",
• series = "Lecture Notes in Computer Science",
• pages = "320--330",
• address = "Vietnam",
• month = dec # " 16-19",
• organisation = "Faculty of Information Technology, Le Quy Don Technical University",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-642-34858-7",
• DOI = "doi:10.1007/978-3-642-34859-4_32",
• size = "11 pages",
• 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

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