Instance-based Classification

Instance-Based Classification is a time series classification approach that belongs to the family of lazy learning methods, wherein classification is performed by directly comparing a query instance to stored labeled instances using a similarity or distance metric, without constructing an explicit generalization model.

324

Publications

18.9K

Citations

919

Authors

407

Institutions

Publications per year

2017–2026

96

Authors

919

  1. CG
    Cleotilde González

    Carnegie Mellon University, United States

    6 publications · 244 citations

    6

    h-index

  2. TM
    Tony Martinez

    Brigham Young University, United States

    5 publications · 75 citations

    5

    h-index

  3. DW
    D.R. Wilson

    Brigham Young University, United States

    5 publications · 90 citations

    5

    h-index

  4. NG
    Nicolás García‐Pedrajas

    University of Córdoba, Spain

    5 publications · 206 citations

    5

    h-index

  5. DW
    David W. Aha

    United States Naval Research Laboratory, United States

    4 publications · 301 citations

    4

    h-index

1–5 of 919

Venues

  1. Intelligent Data Analysis

    IDA · 9 publications · 667 citations

    9

    h-index

  2. Information Sciences

    8 publications · 251 citations

    8

    h-index

  3. Machine Learning

    7 publications · 482 citations

    7

    h-index

  4. arXiv (Cornell University)

    Repository · 7 publications · 287 citations

    7

    h-index

  5. Expert Systems with Applications

    6 publications · 723 citations

    6

    h-index

1–5