Boosting of Association Rules for Robust Emergency Detection

被引：1

作者：

Cipolla, Emanuele ^{[1
]}

Vella, Filippo ^{[1
]}

机构：

[1] Natl Res Council Italy, Inst High Performance Comp & Networking ICAR, Palermo, Italy

来源：

2015 11TH INTERNATIONAL CONFERENCE ON SIGNAL-IMAGE TECHNOLOGY & INTERNET-BASED SYSTEMS (SITIS) | 2015年

关键词：

Big Data; KDD; Disaster prevention; Association rules; AdaBoost;

D O I：

10.1109/SITIS.2015.105

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The use of association rules extracted from daily geophysical measures allows for the detection of previously unknown connections between events, including emergency conditions. 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.

引用

页码：185 / 191

页数：7

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