The use of association rules extracted from daily geophysical measures allows for the detection of previously unknown connections between events, including emergency con- ditions. While these rules imply that the presence of a given symbol occurs while a second one is present, their classification performance may vary with respect to test data. We propose to build strong classifiers out of simpler association rules: their use shows promising results with respect to their accuracy.

Boosting of Association Rules for Robust Emergency Detection

Emanuele Cipolla;Filippo Vella
2015

Abstract

The use of association rules extracted from daily geophysical measures allows for the detection of previously unknown connections between events, including emergency con- ditions. While these rules imply that the presence of a given symbol occurs while a second one is present, their classification performance may vary with respect to test data. We propose to build strong classifiers out of simpler association rules: their use shows promising results with respect to their accuracy.
2015
Istituto di Calcolo e Reti ad Alte Prestazioni - ICAR
Istituto di Calcolo e Reti ad Alte Prestazioni - ICAR
Big Data; KDD; Disaster pre
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/306563
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