Reference Hub

This research has been cited in:

Conference
Adaptive Shapelet Selection for Time Series Classification2023 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD57460.2023.10152664
Conference
Selecting Superior Candidates from a Suitable Set: A Selective Extraction Algorithm for Accelerating Shapelet Discovery in Time Series Data2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD.2019.8791861
Conference
Points2Shapelets: A Salience-Guided Shapelets Selection Approach to Time Series Classification2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9892079
Article
A just-in-time shapelet selection service for online time series classificationComputer Networks10.1016/j.comnet.2019.04.020
Conference
Profile-matrix-based shapelet discovery for time series binary classification2022 18th International Conference on Computational Intelligence and Security (CIS)10.1109/CIS58238.2022.00069
Article
Forest based on Interval Transformation (FIT): A time series classifier with adaptive featuresExpert Systems with Applications10.1016/j.eswa.2022.118923
Article
A Shapelet Selection Algorithm for Time Series Classification: New DirectionsProcedia Computer Science10.1016/j.procs.2018.03.025
Article
Energy analysis of Internet of things data mining algorithm for smart green communication networksComputer Communications10.1016/j.comcom.2020.01.046
Article
W-TSS: A Wavelet-Based Algorithm for Discovering Time Series ShapeletsSensors10.3390/s21175801
Chapter
A 2D Transform Based Distance Function for Time Series ClassificationCollaborative Computing: Networking, Applications and Worksharing10.1007/978-3-030-12981-1_38
Chapter
A Pervasive Multi-physiological Signal-Based Emotion Classification with Shapelet Transformation and Decision FusionCollaborative Computing: Networking, Applications and Worksharing10.1007/978-3-030-92635-9_36
Conference
Margin-based Greedy Shapelet Search for Robust Time Series Classification of Imbalanced Data2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671952
Chapter
Fast Shapelet Discovery with Trend Feature SymbolizationComputer Supported Cooperative Work and Social Computing10.1007/978-981-16-2540-4_26
Article
A fast shapelet selection algorithm for time series classificationComputer Networks10.1016/j.comnet.2018.11.031
Article
XG-SF: An XGBoost Classifier Based on Shapelet Features for Time Series ClassificationProcedia Computer Science10.1016/j.procs.2019.01.179
Article
ADARC: An anomaly detection algorithm based on relative outlier distance and biseries correlationSoftware: Practice and Experience10.1002/spe.2756
Article
Discovering shapelets with key points in time series classificationExpert Systems with Applications10.1016/j.eswa.2019.04.062
Chapter
Extracting Distinctive Shapelets with Random Selection for Early ClassificationKnowledge Science, Engineering and Management10.1007/978-3-030-55130-8_41
Article
Shapelet-transformed Multi-channel EEG Channel SelectionACM Transactions on Intelligent Systems and Technology10.1145/3397850
Article
A novel consciousness emotion recognition method using ERP components and MMSEJournal of Neural Engineering10.1088/1741-2552/abea62
Conference
Big Data Service Request Prediction Based on Historical Behavior Time SeriesProceedings of the 2nd International Conference on Big Data Technologies10.1145/3358528.3358584
Chapter
A Method of Emergency Prediction Based on Spatiotemporal Context Time SeriesSpatial Data and Intelligence10.1007/978-3-030-69873-7_2

A Fast Shapelet Discovery Algorithm Based on Important Data Points

Cun Ji, Chao Zhao, Li Pan, Shijun Liu, Chenglei Yang, Lei Wu

Source Title: International Journal of Web Services Research (IJWSR)14(2)

ISSN: 1545-7362|EISSN: 1546-5004|EISBN13: 9781522511120|DOI: 10.4018/IJWSR.2017040104

Cite Article Cite Article

MLA

Ji, Cun, et al. "A Fast Shapelet Discovery Algorithm Based on Important Data Points." IJWSR vol.14, no.2 2017: pp.67-80. http://doi.org/10.4018/IJWSR.2017040104

APA

Ji, C., Zhao, C., Pan, L., Liu, S., Yang, C., & Wu, L. (2017). A Fast Shapelet Discovery Algorithm Based on Important Data Points. International Journal of Web Services Research (IJWSR), 14(2), 67-80. http://doi.org/10.4018/IJWSR.2017040104

Chicago

Ji, Cun, et al. "A Fast Shapelet Discovery Algorithm Based on Important Data Points," International Journal of Web Services Research (IJWSR) 14, no.2: 67-80. http://doi.org/10.4018/IJWSR.2017040104

Export Reference

Favorite Full-Issue Download

View Full Text HTML

View Full Text PDF

Abstract

Time series classification (TSC) has attracted significant interest over the past decade. A shapelet is one fragment of a time series that can represent class characteristics of the time series. A classifier based on shapelets is interpretable, more accurate, and faster. However, the time it takes to find shapelets is enormous. This article will propose a fast shapelet (FS) discovery algorithm based on important data points (IDPs). First, the algorithm will identify IDPs. Next, the subsequence containing one or more IDPs will be selected as a candidate shapelet. Finally, the best shapelets will be selected. Results will show that the proposed algorithm reduces the shapelet discovery time by approximately 14.0% while maintaining the same level of classification accuracy rates.

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.

Username or email: *

Password: *

Forgot individual login password?

Create individual account

A Fast Shapelet Discovery Algorithm Based on Important Data Points

MLA

APA

Chicago

Export Reference

Abstract

Request Access