Class imbalance is a common issue in the community of machine learning and data mining. The class-imbalance distribution can make most classical classification algorithms neglect the significance of the minority class and tend toward the majority class. In this article, we propose a label enhancement method to solve the class-imbalance problem in a graph manner, which estimates the numerical label and trains the inductive model simultaneously. It gives a new perspective on the class-imbalance learning based on the numerical label rather than the original logical label. We also present an iterative optimization algorithm and analyze the computation complexity and its convergence. To demonstrate the superiority of the proposed method, several single-label and multilabel datasets are applied in the experiments. The experimental results show that the proposed method achieves a promising performance and outperforms some state-of-the-art single-label and multilabel class-imbalance learning methods.

Objective: To evaluate systematically the effects of continuous positive airway pressure (CPAP) on blood pressure in patients with resistant hypertension and obstructive sleep apnea (OSA). Methods: The Cochrane Library, PubMed, ScienceDirect, and the Web of Science were searched for studies investigating the effects of CPAP on blood pressure in patients with resistant hypertension and OSA. The selected studies underwent quality assessment and meta-analysis, as well as being tested for heterogeneity. Results: Six randomized controlled trials were included in the meta-analysis. The pooled estimates of the changes in mean systolic blood pressure and mean diastolic blood pressure (as assessed by 24-h ambulatory blood pressure monitoring) were −5.40 mmHg (95% CI: −9.17 to −1.64; p = 0.001; I2 = 74%) and −3.86 mmHg (95% CI: −6.41 to −1.30; p = 0.00001; I2 = 79%), respectively. Conclusions: CPAP therapy can significantly reduce blood pressure in patients with resistant hypertension and OSA.