Benchmarking manifold learning methods on a large collection of datasets

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{Orzechowski:2020:EuroGP,

• author = "Patryk Orzechowski and Franciszek Magiera and Jason Moore",
• title = "Benchmarking manifold learning methods on a large collection of datasets",
• booktitle = "EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming",
• year = "2020",
• month = "15-17 " # apr,
• editor = "Ting Hu and Nuno Lourenco and Eric Medvet",
• series = "LNCS",
• volume = "12101",
• publisher = "Springer Verlag",
• address = "Seville, Spain",
• pages = "135--150",
• organisation = "EvoStar, Species",
• keywords = "genetic algorithms, genetic programming, Manifold learning, Machine learning, Dimensionality reduction, Benchmarking",
• isbn13 = "978-3-030-44093-0",
• broken = "https://www.youtube.com/watch?v=LhIkDj8oKOI",
• DOI = "doi:10.1007/978-3-030-44094-7_9",
• abstract = "Manifold learning, a non-linear approach of dimensionality reduction, assumes that the dimensionality of multiple datasets is artificially high and a reduced number of dimensions is sufficient to maintain the information about the data. a large scale comparison of manifold learning techniques is performed for the task of classification. We show the current standing of genetic programming (GP) for the task of classification by comparing the classification results of two GP-based manifold leaning methods: GP-Mal and ManiGP - an experimental manifold learning technique proposed in this paper. We show that GP-based methods can more effectively learn a manifold across a set of 155 different problems and deliver more separable embeddings than many established methods.",
• notes = "http://www.evostar.org/2020/cfp_eurogp.php Part of \cite{Hu:2020:GP} EuroGP'2020 held in conjunction with EvoCOP2020, EvoMusArt2020 and EvoApplications2020",

}

Genetic Programming entries for Patryk Orzechowski Franciszek Magiera Jason H Moore

Citations