Article thumbnail
Location of Repository

Classification accuracy, sensitivity, and specificity.

By Kosuke Yoshida (4243093), Yu Shimizu (733328), Junichiro Yoshimoto (253281), Masahiro Takamura (733329), Go Okada (344263), Yasumasa Okamoto (82701), Shigeto Yamawaki (82702) and Kenji Doya (82703)


<p>KPLS-Poly(2) followed by LDA achieves the best performance (accuracy = 80.5%, sensitivity = 81.0%, and specificity = 80.0%).</p

Topics: Cell Biology, Neuroscience, Biotechnology, Hematology, Space Science, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, Chemical Sciences not elsewhere classified, Information Systems not elsewhere classified, depression scores, kernel variants, low-dimensional representation, brain regions, resting-state, MRI data, brain activity, loading vectors, motor area, default mode network, Subsequent classification, cause over-fitting, brain imaging data, PLS, squares regression, resonance imaging, data high-dimensionality, problem
Year: 2017
DOI identifier: 10.1371/journal.pone.0179638.g005
OAI identifier:
Provided by: FigShare
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • (external link)
  • Suggested articles

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.