Many real‐world data mining applications have to deal with unlabeled streaming data. They
are unlabeled because the sheer volume of the stream makes it impractical to label a …

AdaBoost is a boosting-based machine learning method under the assumption that the data
in training and testing sets have the same distribution and input feature space. It increases …

A model can be easily influenced by unseen factors in nonstationary environments and fail
to fit dynamic data distribution. In a classification scenario, this is known as a concept drift …

Twitter spam classification is a tough challenge for social media platforms and cyber security
companies. Twitter spam with illegal links may evolve over time in order to deceive filtering …

In this study, we focus on semi-supervised data stream classification tasks. With the advent
of applications that generate vast streams of data, data stream mining algorithms are …

城市固废焚烧(Municipal solid waste incineration, MSWI) 过程受垃圾成分波动,
设备磨损与维修, 季节交替变化等因素的影响而存在概念漂移现象, 这导致用于污染物排放浓度 …

基于数据驱动的软测量模型广泛用于工业过程中产品质量与环保指标等难测参数的在线测量,
该过程中存在的概念漂移问题易导致模型精度下降. 如何有效识别过程概念变化并精准检测漂移 …

Semi-supervised learning (SSL) is a paradigm that has been continuously used in data
classification tasks in datasets that do not have enough labeled instances to train a …

Timely detection of concept drift plays a vital role in ensuring the stability and reliability of
data-driven models. However, existing concept drift detection methods face challenges in …

Inter-city transportation plays an important role in modern cities, and has accumulated
massive spatio-temporal data from various sensors by IoT (Internet of things) technologies …