Data streams are potentially unbounded sequences of instances arriving over time to a
classifier. Designing algorithms that are capable of dealing with massive, rapidly arriving …

In practical applications, imbalanced data has brought great challenges to classification
problems. In this paper, we propose two new methods:(1) a new oversampling method …

Ensemble technique and under-sampling technique are both effective tools used for
imbalanced dataset classification problems. In this paper, a novel ensemble method …

In data-driven bearing fault diagnosis, sufficient fault data are fundamental for algorithm
training and validation. However, only very few fault measurements can be provided in most …

Resampling is the most commonly used method for dealing with imbalanced data, in
addition to modifying the algorithm mechanism, it can, for example, generate new minority …

Class imbalance is one of the important challenges for machine learning because of it's
learning to bias toward the majority classes. The oversampling method is a fundamental …

From a real-world perspective, missing information is an ordinary scenario in data stream.
Generally, missing data generate diverse problems in recognizing the pattern of data (ie …

Rapid breakthroughs in information technologies have driven substantial developments in
artificial intelligence applications, particularly the widespread use of deep learning …

Streaming data incorporates dynamicity due to a nonstationary environment where data
samples may endure class imbalance and change in data distribution over the period …

随着大数据的快速发展, 挖掘有价值的知识可能会面临高维, 大量, 动态数据的影响,
这些复杂数据流的出现会导致分类效果下降. 为了进一步分析数据流集成分类的研究现状和面临 …