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Multi-feature concatenation and multi-classifier stacking: an interpretable and generalizable machine learning method for MDD discrimination with rsfMRI
In addition, the generalizability of MFMC is validated by the good performance when the training and testing subjects are from independent sites.
Accelerated functional brain aging in major depressive disorder: evidence from a large scale fMRI analysis of Chinese participants
The positive brain-PAD observed in participants in China confirms the presence of accelerated brain aging in MDD patients.
A method on selecting reliable samples based on fuzziness in positive and unlabeled learning
Traditional semi-supervised learning uses only labeled instances to train a classifier and then this classifier is utilized to classify unlabeled instances, while sometimes there are only positive instances which are elements of the target concept are available in the labeled set.
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