Deep Learning-Based Imbalanced Classification With Fuzzy Support Vector Machine.

Imbalanced classification is widespread in the fields of medical diagnosis, biomedicine, smart city and Internet of Things. The imbalance of data distribution makes traditional classification methods more biased towards majority classes and ignores the importance of minority class. It makes the traditional classification methods ineffective in imbalanced classification. In this paper, a novel imbalance classification method based on deep learning and fuzzy support vector machine is proposed and named as DFSVM. DFSVM first uses a deep neural network to obtain an embedding representation of the data. This deep neural network is trained by using triplet loss to enhance similarities within classes and differences between classes. To alleviate the effects of imbalanced data distribution, oversampling is performed in the embedding space of the data. In this paper, we use an oversampling method based on feature and center distance, which can obtain more diverse new samples and prevent overfitting. To enhance the impact of minority class, we use a fuzzy support vector machine (FSVM) based on cost-sensitive learning as the final classifier. FSVM assigns a higher misclassification cost to minority class samples to improve the classification quality. Experiments were performed on multiple biological datasets and real-world datasets. The experimental results show that DFSVM has achieved promising classification performance.

[P53 expression in liver tissue of patients with chronic hepatitis B and its influencing factors].

OBJECTIVE: To observe p53 expression in liver tissue of patients with chronic hepatitis B and its influencing factors. METHODS: 17 cases HBeAg-negative chronic hepatitis B patients and 31 cases HBeAg-positive chronic hepatitis B patients were divided into 2 groups. RESULTS: (1) HBeAg-negative chronic hepatitis B patients were older, mostly male and HBV DNA lower. These three indicators between two groups patients appeared statistical difference. Serum markers were no statistical difference between two groups patients except Glo. (2) Pathological inflammation and fibrosis Staging were no statistical difference between two groups patients. p53 expression positive rate and p53 expression semi-quantitative scoring in liver tissue were no statistical difference between the two groups. (3) Logistic regression analysis showed that only liver fibrosis staging (S) is a risk factor for p53 expression. Compared with the S0-1, p53 expression increased by 3.9 times the rate of positive in S > or = 2. CONCLUSION: Liver fibrosis staging in patients with chronic hepatitis B is a risk factor for p53 positive expression in liver.