Multi-modality Hierarchical Recall based on GBDTs for Bipolar Disorder Classification

Abstract

In this paper, we propose a novel hierarchical recall model fusing multiple modality (including audio, video and text) for bipolar disorder classification, where patients with different mania level are recalled layer-by-layer. To address the complex distribution on the challenge data, the proposed framework utilizes multi-model, multi-modality and multi-layer to perform domain adaptation for each patient and hard sample mining for special patients. The experimental results show that our framework achieves competitive performance with Unweighed Average Recall (UAR) of 57.41% on the test set, and 86.77% on the development set.

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