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Regular version of the site

Book chapter

Boosting connectome classification via combination of geometric and topological normalizations

P. 1-4.
Petrov D., Dodonova Y., Zhukov L. E., Belyaev M.

The structural connectome classification is a challenging task due to a small sample size and high dimensionality of feature space. In this paper, we propose a new data prepossessing method that combines geometric and topological connectome normalization and significantly improves classification results. We validate this approach by performing classification between autism spectrum disorder and normal development connectomes in children and adolescents. We demonstrate a significant enhancement in performance using weighted and normalized data over the best available model (boosted decision trees) trained on baseline features.