Abstract
Depression is an affective disorder that poses a serious threat to both mental and physical health. Utilizing fuzzy-based neural network models for the identification and screening of depression can facilitate early intervention and treatment. Intuitionistic fuzzy stochastic configuration networks (IFSCNs) utilize a cost-sensitive learning framework, which enhances generalization performance for solving binary classification problems. However, IFSCNs ignore the impact of the relative neighborhood density of imbalanced samples with outliers. To learn more discriminant information from class imbalance depression recognition task, in this article, we propose a novel deep ensemble stochastic configuration network via graph intuitionistic fuzzy, termed as DeSCN-GIF. Specifically, we first use graph-based intuitionistic fuzzy method to determine the membership and nonmembership functions through the relative neighborhood density of imbalanced samples; moreover, an incremental self-ensemble deep stochastic configuration framework is presented to learn multilevel discriminative features, in which graph intuitionistic fuzzy cost-sensitive least squares loss function and weighted supervision mechanisms are applied to determine the parameters of DeSCN-GIF. Experimental results on Chinese syllable-based imbalanced depression voice datasets show that DeSCN-GIF has better binary classification performance compared to other learning models such as IFSCN, DSCN, SCN, GE-IFRVFL-CIL, IFRVFL, EDRVFL, DRVFL, RVFL, and DIFL-TSVM.