A Feature Selection Method for Air Quality Forecasting View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2010

AUTHORS

David Hutchison , Takeo Kanade , Josef Kittler , Jon M. Kleinberg , Friedemann Mattern , John C. Mitchell , Moni Naor , Oscar Nierstrasz , C. Pandu Rangan , Bernhard Steffen , Madhu Sudan , Demetri Terzopoulos , Doug Tygar , Moshe Y. Vardi , Gerhard Weikum , Luca Mesin , Fiammetta Orione , Riccardo Taormina , Eros Pasero

ABSTRACT

Local air quality forecasting can be made on the basis of meteorological and air pollution time series. Such data contain redundant information. Partial mutual information criterion is used to select the regressors which carry the maximal non redundant information to be used to build a prediction model. An application is shown regarding the forecast of PM10 concentration with one day of advance, based on the selected features feeding an artificial neural network. More... »

PAGES

489-494

Book

TITLE

Artificial Neural Networks – ICANN 2010

ISBN

978-3-642-15824-7
978-3-642-15825-4

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-15825-4_66

DOI

http://dx.doi.org/10.1007/978-3-642-15825-4_66

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1018763839


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