A Large Margin Deep Neural Network for Sepsis Classification

Yiwen Wang


Data in clinical medical often exhibit highly imbalanced class distribution. To mitigate this issue, current approaches based on deep neural network typically follow the strategies such as re-sampling and cost-sensitive learning. However, these methods neglect the underlying data structure. We propose a novel sampling method which explicitly enforces constraints on the intra-class and inter-class margins. To achieve this goal, we need 3 steps. In step 1, we first apply K-means for each class to obtain the initial clusters. In step 2, for each sample in the training set, we propose to ensure the following relationship holds $$D(f(x_{i}),f(x_{i}^{a}))