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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)

Abstract

<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: oai:figshare.com:article/5199178
Provided by: FigShare
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