Directly Constructing Multiple Features for Classification with Missing Data using Genetic Programming with Interval Functions

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Tran:2016:GECCOcomp,

• author = "Cao Truong Tran and Mengjie Zhang and Peter Andreae and Bing Xue",
• title = "Directly Constructing Multiple Features for Classification with Missing Data using Genetic Programming with Interval Functions",
• booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation",
• year = "2016",
• editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing",
• pages = "69--70",
• keywords = "genetic algorithms, genetic programming: Poster",
• month = "20-24 " # jul,
• organisation = "SIGEVO",
• address = "Denver, USA",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• isbn13 = "978-1-4503-4323-7",
• DOI = "doi:10.1145/2908961.2909002",
• abstract = "Missing values are a common issue in many industrial and real-world datasets. Genetic programming-based multiple feature construction (GPMFC) is a recent promising filter approach to constructing multiple features for classification using genetic programming (GP). GPMFC has been demonstrated to improve classification performance and reduce the complexity of many decision trees and rule-based classifiers, but it cannot work with missing data. To deal with missing data, this paper propose IGPMFC, an extension of GPMFC that use interval functions as the GP function set to directly construct multiple features for classification with missing data. Empirical results on five datasets and four classifiers show that IGPMFC can substantially improve the performance and reduce the complexity of the classifiers when faced with missing data.",
• notes = "Distributed at GECCO-2016.",

}

Genetic Programming entries for Cao Truong Tran Mengjie Zhang Peter Andreae Bing Xue

Citations