NDLI: Classification of Multi-dimensional Streaming Time Series by Weighting Each Classifier's Track Record

Content Provider	IEEE Xplore Digital Library
Author	Bing Hu Yanping Chen Zakaria, J. Ulanova, L. Keogh, E.
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Univ. of California, Riverside, Riverside, CA, USA (Bing Hu; Yanping Chen; Zakaria, J.; Ulanova, L.; Keogh, E.)
Abstract	Extensive research on time series classification in the last decade has produced fast and accurate algorithms for the single-dimensional case. However, the increasing prevalence of inexpensive sensors has reinforced the need for algorithms to handle multi-dimensional time series. For example, modern smartphones have at least a dozen sensors capable of producing streaming time series, and hospital-based (and increasingly, home-based) medical devices can produce time series streams from more than twenty sensors. The two most common ways to generalize from single to multi-dimensional data are to use all the streams or just the single best stream as determined at training time. However, as we show here, both approaches can be very brittle. Moreover, neither approach exploits the observation that different sensors may be considered "experts" on different classes. In this work, we introduce a novel framework for multi-dimensional time series classification that weights the class prediction from each time series stream. These weights are based not only on each stream's previous track record on the class it is currently predicting, but also on the distance from the unlabeled object. As we demonstrate with extensive experiments on real data, our method is more accurate than current approaches and particularly robust in the face of concept drift or sensor noise.
Sponsorship	Toshiba
Starting Page	281
Ending Page	290
File Size	515493
Page Count	10
File Format	PDF
ISBN	9780769551081
ISSN	15504786
DOI	10.1109/ICDM.2013.33
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Time series analysis Sensors Training Wrist Footwear Training data Classification algorithms classification multi-dimensional time series
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Classification of Multi-Dimensional Streaming Time Series by Weighting each Classifier’s Track Record

Classification of multi-dimensional streaming time series by weighting each classifier’s track record.

Fuzzy classification of time series data

Weighted feature-based classification of time series data

A versatile approach to classification of multivariate time series data

Multi-dimensional time warping based on complexity invariance and its application in sports evaluation

Trees Weighting Random Forest Method for Classifying High-Dimensional Noisy Data

Active Learning for Multivariate Time Series Classification with Positive Unlabeled Data

EEG-based eight class, single trial classification of imaginary wrist movements

Classification of Multi-dimensional Streaming Time Series by Weighting Each Classifier's Track Record

Similar Documents

Classification of Multi-Dimensional Streaming Time Series by Weighting each Classifier’s Track Record

Classification of multi-dimensional streaming time series by weighting each classifier’s track record.

Fuzzy classification of time series data

Weighted feature-based classification of time series data

A versatile approach to classification of multivariate time series data

Multi-dimensional time warping based on complexity invariance and its application in sports evaluation

Trees Weighting Random Forest Method for Classifying High-Dimensional Noisy Data

Active Learning for Multivariate Time Series Classification with Positive Unlabeled Data

EEG-based eight class, single trial classification of imaginary wrist movements

Classification of Multi-dimensional Streaming Time Series by Weighting Each Classifier's Track Record