Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data
| Autor(en): | Thölke, Philipp Ramos, Yorguin Jose Mantilla Abdelhedi, H. Maschke, Charlotte Dehgan, Arthur Harel, Yann Kemtur, Anirudha Berrada, Loubna Mekki Sahraoui, Myriam Young, Tammy Hadid, Vanessa Combrisson, Etienne O'Byrne, Jordan Jerbi, Karim |
Stichwörter: | Psychology; Random forest; Electroencephalography; Pattern recognition (psychology); Mathematics; Biochemistry; Support vector machine; Gene; Robustness (evolution); Decoding methods; Binary classification; Economics; Operations management; Magnetoencephalography; Metric (unit); Management; Binary number; Algorithm; Computer science; Class (philosophy); Chemistry; Performance metric; Receiver operating characteristic; Machine learning; Artificial intelligence; Psychiatry; Arithmetic | Erscheinungsdatum: | 2022 | Herausgeber: | Cold Spring Harbor Laboratory | Journal: | Cold Spring Harbor Laboratory - bioRxiv | DOI: | https://doi.org/10.1101/2022.07.18.500262 |
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geprüft am 19.05.2024
