Shapelet Features

Shapelet Features is a discriminative time series subsequence that captures distinctive shape-based patterns, distinguished by its ability to serve as a feature for classification tasks through its high similarity to specific temporal motifs within the data.

94

Publications

6.8K

Citations

311

Authors

149

Institutions

Publications per year

2017–2026

56

Authors

311

  1. CJ
    Cun Ji

    Shandong Normal University, China

    5 publications · 182 citations

    5

    h-index

  2. LZ
    Lipeng Zhu

    Hunan University, China

    4 publications · 259 citations

    4

    h-index

  3. YC
    Yixin Chen

    Washington University in St. Louis, United States

    4 publications · 631 citations

    4

    h-index

  4. SL
    Shijun Liu

    Shandong University, China

    4 publications · 166 citations

    4

    h-index

  5. JW
    Jia Wu

    Macquarie University, Australia

    4 publications · 257 citations

    4

    h-index

1–5 of 311

Venues

  1. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 5 publications · 608 citations

    5

    h-index

  2. Expert Systems with Applications

    3 publications · 177 citations

    3

    h-index

  3. Computer Networks

    3 publications · 113 citations

    3

    h-index

  4. Pattern Recognition

    3 publications · 101 citations

    3

    h-index

  5. IEEE Transactions on Industrial Informatics

    3 publications · 136 citations

    3

    h-index

1–5