Despite significant successes achieved in knowledge discovery, traditional machine
learning methods may fail to obtain satisfactory performances when dealing with complex …

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

Ensemble learning has gained success in machine learning with major advantages over
other learning methods. Bagging is a prominent ensemble learning method that creates …

Today's text classifiers inevitably suffer from unintended dataset biases, especially the
document-level label bias and word-level keyword bias, which may hurt models' …

Background Imbalance between positive and negative outcomes, a so-called class
imbalance, is a problem generally found in medical data. Despite various studies, class …

Today, classification of imbalanced datasets, in which the samples belonging to one class is
more than the samples pertaining to other classes, has been paid much attention owing to …

The classification on imbalanced data sets is a great challenge in machine learning. In this
paper, a geometric structural ensemble (GSE) learning framework is proposed to address …

不平衡数据是机器学习中普遍存在的问题并得到广泛研究, 即少数类的样本数量远远小于多数类
样本的数量. 传统基于最小化错误率方法的不足在于: 分类结果会倾向于多数类 …

Numerous imbalanced datasets exist in modern machine learning dilemmas. Challenges of
generalization and fairness stem from the existence of underrepresented classes with …

Many daily applications are generating massive amount of data in the form of stream at an
ever higher speed, such as medical data, clicking stream, internet record and banking …