Network traffic classification has long been a pivotal topic in network security. In the past two
decades, methods like port-based classification, deep packet inspection, and machine …

A novel linear multivariate decision tree classifier, Binary Decision Tree based on K-means
Splitting (BDTKS), is presented in this paper. The unsupervised K-means clustering is …

Hierarchical classification identifies a sample from the root node to a leaf node along the
hierarchical structures of labels. It is often difficult to perform leaf-node prediction owing to …

To ensure the operational safety and reliability, fault recognition of complex systems is
becoming an essential process in industrial systems. However, the existing recognition …

传统机器学习方法和深度神经网络在训练模型的过程中都需要大量标记样本作为支撑,
然而标记大量样本是一个耗费巨大的过程, 并且真实场景变化莫测, 获得所有类别的标记样本是 …

The hierarchical classification with an imbalance class problem is a challenge for in
machine learning, and is caused by data with an uneven distribution. Learning from an …

Hierarchical classification is a research hotspot in machine learning due to the widespread
existence of data with hierarchical class structures. Existing hierarchical classification …

Few-shot object detection (FSOD) is to detect objects with a few examples. However,
existing FSOD methods do not consider hierarchical fine-grained category structures of …

Embedded devices are generally small, battery-powered computers with limited hardware
resources. It is difficult to run deep neural networks (DNNs) on these devices, because …

Hierarchical classification is significant for big data, where the original task is divided into
several sub-tasks to provide multi-granularity predictions based on a tree-shape label …