Automated feature selection for a machine learning approach toward modeling a mosquito distribution

Ralf Wieland, Antje Kerkow, Linus Früh, Helge Kampen and Doreen Walther

Ecological Modelling, 2017, vol. 352, issue C, 108-112

Abstract: This paper introduces a data science method to determine a set of features for training a vector support machine (SVM). The SVM is used to model the relationship between the distribution of one particular invasive mosquito species and climate data. Two biologists selected training data on the basis of their domain expertise. This was compared with the result of the data science simulation. The paper then explores the possible uses of data science to generate new knowledge as well as to identify the weaknesses of this technique.

Keywords: Data science; Machine learning; Genetic optimization; Cluster computing; Mosquito dispersion modeling (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304380016307906
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:352:y:2017:i:c:p:108-112

DOI: 10.1016/j.ecolmodel.2017.02.029

Access Statistics for this article

Ecological Modelling is currently edited by Brian D. Fath

More articles in Ecological Modelling from Elsevier
Bibliographic data for series maintained by Catherine Liu ().