A General Feature Engineering Wrapper for Machine Learning Using epsilon-Lexicase Survival

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{LaCava:2017:EuroGP,

• author = "William {La Cava} and Jason Moore",
• title = "A General Feature Engineering Wrapper for Machine Learning Using epsilon-Lexicase Survival",
• 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 = "80--95",
• organisation = "species",
• keywords = "genetic algorithms, genetic programming, Feature selection, Representation learning, Regression",
• isbn13 = "978-3-319-55695-6",
• URL = "https://cavalab.org/assets/papers/La_Cava_and_Moore_-_2017_-_A_General_Feature_Engineering_Wrapper_for_Machine_.pdf",
• DOI = "doi:10.1007/978-3-319-55696-3_6",
• code_url = "https://github.com/lacava/few",
• size = "16 pages",
• abstract = "We propose a general wrapper for feature learning that interfaces with other machine learning methods to compose effective data representations. The proposed feature engineering wrapper (FEW) uses genetic programming to represent and evolve individual features tailored to the machine learning method with which it is paired. In order to maintain feature diversity, e-lexicase survival is introduced, a method based on epsilon-lexicase selection. This survival method preserves semantically unique individuals in the population based on their ability to solve difficult subsets of training cases, thereby yielding a population of uncorrelated features. We demonstrate FEW with five different off-the-shelf machine learning methods and test it on a set of real-world and synthetic regression problems with dimensions varying across three orders of magnitude. The results show that FEW is able to improve model test predictions across problems for several ML methods. We discuss and test the scalability of FEW in comparison to other feature composition strategies, most notably polynomial feature expansion.",
• notes = "Part of \cite{Castelli:2017:GP} EuroGP'2017 held inconjunction with EvoCOP2017, EvoMusArt2017 and EvoApplications2017",

}

Genetic Programming entries for William La Cava Jason H Moore

Citations