Консолидация различных вариантов сетевых структур мозга при решении задачи классификации нормы и патологии
We solve a task of classifying autism spectrum disorder versus normal controls based on structural brain networks (connectomes). We compare different approaches to machine learning in a nspecial conditions when each subject is represented by a set of connectomes obtained after applying different weightings and normalizations to the original dataset. We consider two algorithms of two-level classifications: stacking and blending of the models trained on the different types of the data. We also build a discriminative fusion classifier which is a logistic regression on the weighted combination of the varios types of connectomes. The best classification quality (ROC AUC of 0.8) is obtained for blending which is a weighted combination of the logistic regression models; this model performes better than the first-level individual models.