Multi-view learning-based data proliferator for boosting classification using highly imbalanced classes


Graa O., Rekık I.

JOURNAL OF NEUROSCIENCE METHODS, vol.327, 2019 (Journal Indexed in SCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 327
  • Publication Date: 2019
  • Doi Number: 10.1016/j.jneumeth.2019.108344
  • Title of Journal : JOURNAL OF NEUROSCIENCE METHODS

Abstract

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.