Rare events, especially those that could potentially negatively impact society, often require
humans' decision-making responses. Detecting rare events can be viewed as a prediction …

Class imbalance is one of the challenges of machine learning and data mining fields.
Imbalance data sets degrades the performance of data mining and machine learning …

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 …

Corporate bankruptcy prediction is very important for creditors and investors. Most literature
improves performance of prediction models by developing and optimizing the quantitative …

Class imbalance problems, where the number of samples in each class is unequal, is
prevalent in numerous real world machine learning applications. Traditional methods which …

Human activity recognition is an imbalance classification problem in essence since various
human actions may occur at different frequencies. Traditional ensemble class imbalance …

Wafer defect detection using an intelligent system is an approach of quality improvement in
semiconductor manufacturing that aims to enhance its process stability, increase production …

Many real-world classification problems such as fraud detection, intrusion detection, churn
prediction, and anomaly detection suffer from the problem of imbalanced datasets …

Bagging, as a commonly-used class imbalance learning method, combines resampling
techniques with ensemble learning to provide a strong classifier with high generalization for …

如何获得更加精确的分类效果一直是机器学习领域的重要研究内容, 现有大多数分类器都是针对
平衡的数据集来设计的. 虽然平衡的数据训练出来的分类模型能取得较好的正负样本分类正确率 …