Evolving Spatially Aggregated Features from Satellite Imagery for Regional Modeling

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

@InProceedings{Kriegman:2016:PPSN,

• author = "Sam Kriegman and Marcin Szubert and Josh C. Bongard and Christian Skalka",
• title = "Evolving Spatially Aggregated Features from Satellite Imagery for Regional Modeling",
• booktitle = "14th International Conference on Parallel Problem Solving from Nature",
• year = "2016",
• editor = "Julia Handl and Emma Hart and Peter R. Lewis and Manuel Lopez-Ibanez and Gabriela Ochoa and Ben Paechter",
• volume = "9921",
• series = "LNCS",
• pages = "707--716",
• address = "Edinburgh",
• month = "17-21 " # sep,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, Spatial aggregation, Feature construction, Symbolic regression",
• isbn13 = "978-3-319-45823-6",
• DOI = "doi:10.1007/978-3-319-45823-6_66",
• size = "10 pages",
• abstract = "Satellite imagery and remote sensing provide explanatory variables at relatively high resolutions for modelling geospatial phenomena, yet regional summaries are often desirable for analysis and actionable insight. In this paper, we propose a novel method of inducing spatial aggregations as a component of the machine learning process, yielding regional model features whose construction is driven by model prediction performance rather than prior assumptions. Our results demonstrate that Genetic Programming is particularly well suited to this type of feature construction because it can automatically synthesize appropriate aggregations, as well as better incorporate them into predictive models compared to other regression methods we tested. In our experiments we consider a specific problem instance and real-world dataset relevant to predicting snow properties in high-mountain Asia.",
• notes = "snow melt runoff. NASA. Afganistan. PPSN2016",

}

Genetic Programming entries for Sam Kriegman Marcin Szubert Josh C Bongard Christian Skalka

Citations