Using Feature Clustering for GP-Based Feature Construction on High-Dimensional Data

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@InProceedings{Tran:2017:EuroGP,

• author = "Binh Tran and Bing Xue and Mengjie Zhang",
• title = "Using Feature Clustering for {GP}-Based Feature Construction on High-Dimensional Data",
• booktitle = "EuroGP 2017: Proceedings of the 20th European Conference on Genetic Programming",
• year = "2017",
• month = "19-21 " # apr,
• editor = "Mauro Castelli and James McDermott and Lukas Sekanina",
• series = "LNCS",
• volume = "10196",
• publisher = "Springer Verlag",
• address = "Amsterdam",
• pages = "210--226",
• organisation = "species",
• keywords = "genetic algorithms, genetic programming, Feature construction, Feature clustering, Classification, High-dimensional data",
• isbn13 = "978-3-319-55695-6",
• DOI = "doi:10.1007/978-3-319-55696-3_14",
• size = "17 pages",
• abstract = "Feature construction is a pre-processing technique to create new features with better discriminating ability from the original features. Genetic programming (GP) has been shown to be a prominent technique for this task. However, applying GP to high-dimensional data is still challenging due to the large search space. Feature clustering groups similar features into clusters, which can be used for dimensionality reduction by choosing representative features from each cluster to form the feature subset. Feature clustering has been shown promising in feature selection; but has not been investigated in feature construction for classification. This paper presents the first work of using feature clustering in this area. We propose a cluster-based GP feature construction method called CGPFC which uses feature clustering to improve the performance of GP for feature construction on high-dimensional data. Results on eight high-dimensional datasets with varying difficulties show that the CGPFC constructed features perform better than the original full feature set and features constructed by the standard GP constructor based on the whole feature set.",
• notes = "Part of \cite{Castelli:2017:GP} EuroGP'2017 held inconjunction with EvoCOP2017, EvoMusArt2017 and EvoApplications2017",

}

Genetic Programming entries for Binh Ngan Tran Bing Xue Mengjie Zhang

Citations