Accurate prediction of the mechanical properties of concrete has been a concern since
these properties are often required by design codes. The emergence of new concrete …

Unbalanced classification is an essential machine learning task, which has attracted
widespread attention from both the academic and industrial communities due mainly to its …

With the continuous emergence of various network attacks, it is becoming more and more
important to ensure the security of the network. Intrusion detection, as one of the important …

Imbalanced data with a skewed class distribution are common in many real-world
applications. Deep Belief Network (DBN) is a machine learning technique that is effective in …

In an actual industrial scenario, machines typically operate normally for the majority of the
time, with malfunctions occurring only occasionally. As a result, there is very little recorded …

Many real-world machine learning applications require building models using highly
imbalanced datasets. Usually, in medical datasets, the healthy patients or samples are …

Context: In practice, software datasets tend to have more non-defective instances than
defective ones, which is referred to as the class imbalance problem in software defect …

Aero engine fault diagnosis is very important to ensure flight safety. However, the lack of
fault data limits aero engine fault diagnosis. Fortunately, transfer learning can transfer data …

摘要极限学习机(ExtremeLearningMachine, ELM) 作为前馈神经网络学习中一种全新的训练
框架, 在行为识别, 情感识别和故障诊断等方面被广泛应用, 引起了各个领域的高度关注和深入 …

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 …