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A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification


Gifted children are able to learn in a more advanced way than others, probably due to neurophysiological differences in the communication efficiency in neural pathways. Topological features contribute to understanding the correlation between the brain structure and intelligence. Despite decades of neuroscience research using MRI, methods based on brain region connectivity patterns are limited by MRI artifacts, which therefore leads to revisiting MRI morphometric features, with the aim of using them to directly identify gifted children instead of using brain connectivity. However, the small, high-dimensional morphometric feature dataset with outliers makes the task of finding good classification models challenging. To this end, a hybrid method is proposed that combines tensor completion and feature selection methods to handle outliers and then select the discriminative features. The proposed method can achieve a classification accuracy of 93.1%, higher than other existing algorithms, which is thus suitable for the small MRI datasets with outliers in supervised classification scenarios.

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Corresponding authors

Correspondence to Feng Duan, Zhe Sun or Jordi Solé-Casals.

Additional information

This work was supported by the National Key R&D Program of China (Grant No. 2017YFE0129700), the National Natural Science Foundation of China (Key Program) (Grant No. 11932013), the National Natural Science Foundation of China (Grant No. 61673224), the Tianjin Natural Science Foundation for Distinguished Young Scholars (Grant No. 18JCJQJC46100), and the Tianjin Science and Technology Plan Project (Grant No. 18ZXJMTG00260). J.S-C. work is also based upon work from COST Action CA18106, supported by COST (European Cooperation in Science and Technology). C.F.C work was supported by grants PICT 2017-3208 and UBACYT 20020170100192BA (Argentina).

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Zhang, J., Feng, F., Han, T. et al. A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification. Sci. China Technol. Sci. 64, 1863–1871 (2021).

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  • gifted children identification
  • morphometric features
  • tensor completion
  • feature selection