Paper Info
Title
Boosting for Real-Time Multivariate Time Series Classification.
Abstract
Multivariate time series (MTS) is useful for detecting abnormity cases in healthcare area. In this paper, we propose an ensemble boosting algorithm to classify abnormality surgery time series based on learning shapelet features. Specifically, we first learn shapelets by logistic regression from multivariate time series. Based on the learnt shapelets, we propose a MTS ensemble boosting approach when the time series arrives as stream fashion. Experimental results on a real-world medical dataset demonstrate the effectiveness of the proposed methods.
Year
Venue
Field
2017
THIRTY-FIRST AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE
Computer science,Multivariate statistics,Artificial intelligence,Boosting (machine learning),Machine learning,Time series classification
DocType
Citations 
PageRank 
Conference
2
0.37
References 
Authors
0
2
Name
Order
Citations
PageRank
Hai-Shuai Wang16213.11
Jun Wu212515.66