Evolutionary Linear Discriminant Analysis for Multiclass Classification Problems

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

@InProceedings{Korns:2017:GECCO,

• author = "Michael F. Korns",
• title = "Evolutionary Linear Discriminant Analysis for Multiclass Classification Problems",
• booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion",
• series = "GECCO '17",
• year = "2017",
• isbn13 = "978-1-4503-4939-0",
• address = "Berlin, Germany",
• pages = "233--234",
• size = "2 pages",
• URL = "http://doi.acm.org/10.1145/3067695.3075964",
• DOI = "doi:10.1145/3067695.3075964",
• acmid = "3075964",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, linear discriminant analysis, symbolic regression and classification",
• abstract = "This paper implements Linear Discriminant Analysis (LDA) together with genetic programming symbolic classification for financial multiclass classification problems. A very brief description of the matrix theory of LDA is included. The implementation details in an industrial strength multiclass classification system are discussed. The algorithm is tested using statistically correct, out of sample training and testing. The algorithm's behaviour is demonstrated on real world problems previously published as UCI test suites and financial real world problems.",
• month = "15-19 " # jul,
• notes = "Also known as \cite{Korns:2017:ELD:3067695.3075964} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)",

}

Genetic Programming entries for Michael Korns

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