Model performance in ROC space across different algorithms and task conditions.

Model performance in ROC space across different algorithms and task conditions.

<p>Performance of models in ROC space for datasets under all conditions (firm surface with eyes open and eyes closed, foam surface with eyes open and eyes closed), using all data processing methods (original time domain and CMCI method), and all classification methods (Bayesian, Ranking Tree,...

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محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Yuqi Cheng (428511) (author)
مؤلفون آخرون: Dawei Wu (519682) (author), Ying Wu (19057) (author), Youcai Guo (20642726) (author), Xinze Cui (20642729) (author), Pengquan Zhang (20642732) (author), Jie Gao (10266) (author), Yanming Fu (1844281) (author), Xin Wang (91924) (author)
منشور في: 2025
الموضوعات:
Environmental Sciences not elsewhere classified
Biological Sciences not elsewhere classified
various athletic tasks
test split method
support vector machine
remains unclear whether
ranking forest yielded
participant &# 8217
novel classification method
hard support surfaces
accessible measurement methods
13 elite athletes
multidimensional approach appears
xlink "> balance
whereas cmci performed
various indicators proposed
roc results showed
common approach
balance differences
balance ability
balance abilities
traditional time
sports science
significant challenge
second trials
postural control
logistic regression
highly suitable
fully explain
eyes open
complex interactions
classic train
best overall
analyses alone
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