Temporal data classification using linear classifiers

被引：9

作者：

Revesz, Peter ^{[1
]}

Triplet, Thomas ^{[2
]}

机构：

[1] Univ Nebraska, Dept Comp Sci & Engn, Lincoln, NE 68588 USA

[2] Concordia Univ, Dept Comp Sci, Montreal, PQ H3G 1M8, Canada

来源：

INFORMATION SYSTEMS | 2011年 / 36卷 / 01期

关键词：

Classification integration; Constraint database; Datalog; Data integration; Decision tree; Reclassification; SVM;

D O I：

10.1016/j.is.2010.06.006

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Data classification is usually based on measurements recorded at the same time. This paper considers temporal data classification where the input is a temporal database that describes measurements over a period of time in history while the predicted class is expected to occur in the future. We describe a new temporal classification method that improves the accuracy of standard classification methods. The benefits of the method are tested on weather forecasting using the meteorological database from the Texas Commission on Environmental Quality and on influenza using the Google Flu Trends database. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：30 / 41

页数：12

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