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Multi-view Learning-based Data Proliferator for Boosting Classification Using Highly Imbalanced Classes by Olfa Graa; Islem Rekik is a Neuroscience article available to read on EtoBox.

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## Background: Multi-view data representation learning explores the relationship between the views and provides rich complementary information that can improve computer-aided diagnosis. specifically, existing machine learning methods devised to automate neurological disorder diagnosis using brain data provided new insights into how a particular disorder such as autism spectrum disorder (asd) alters the brain construct. however, the performance of machine learning methods highly depends on the size of the training samples from both classes. in a real-world clinical setting, such medical data is very expensive and challenging to collect, might (i) suffer from several limitations such as imbalanced classes and (ii) have non-heterogeneous distribution when derived from multi-view brain representations. ## New method: To the best of our knowledge, the problem of imbalanced and multi-view data classification remains unexplored in the field of network neuroscience. to fill this gap, we propose a multi-view learning-based data proliferator (mv-leap) that enables the classification of imbalanced multi-view representations. mv-leap comprises two key steps. first, a manifold learning-based pr

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Author

Olfa Graa; Islem Rekik

Published

2019

Language

EN

Field

Neuroscience (Life Sciences)