Biological Strategies ParetoGP Enables Analysis of Wide and Ill-Conditioned Data from Nonlinear Systems

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{Kotanchek:2022:GPTP,

• author = "Mark Kotanchek and Theresa Kotanchek and Kelvin Kotanchek",
• title = "Biological Strategies {ParetoGP} Enables Analysis of Wide and Ill-Conditioned Data from Nonlinear Systems",
• booktitle = "Genetic Programming Theory and Practice XIX",
• year = "2022",
• editor = "Leonardo Trujillo and Stephan M. Winkler and Sara Silva and Wolfgang Banzhaf",
• series = "Genetic and Evolutionary Computation",
• pages = "91--116",
• address = "Ann Arbor, USA",
• month = jun # " 2-4",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-981-19-8459-4",
• DOI = "doi:10.1007/978-981-19-8460-0_5",
• abstract = "Genetic, proteomic, and other biologically derived data sets are often ill-conditioned with many more variables than data records. Furthermore, the variables are often highly correlated as well as coupled. These attributes make such data sets very difficult to analyze with conventional statistical and machine learning techniques. The ParetoGP approach implemented within DataModeler exploring the trade-off between model complexity and accuracy enables attacking such data sets with dual benefits of identifying key variables, associations, and metavariables along with providing concise, explainable, and human-interpretable predictive models. Transparency of key variables, model structures, and response behaviors provide a substantial benefit relative to conventional machine learning and the associated black-box models. In this chapter, we describe the analysis methodology and highlight benefits using available biological data sets.",
• notes = "Part of \cite{Banzhaf:2022:GPTP} published after the workshop in 2023",

}

Genetic Programming entries for Mark Kotanchek Theresa Kotanchek Kelvin Kotanchek

Citations