Learning from data streams in the presence of concept drift is among the biggest challenges
of contemporary machine learning. Algorithms designed for such scenarios must take into …

Mining data streams is among most vital contemporary topics in machine learning. Such
scenario requires adaptive algorithms that are able to process constantly arriving instances …

In this paper, we propose a new research problem on active learning from data streams,
where data volumes grow continuously, and labeling all data is considered expensive and …

Class label noise is a critical component of data quality that directly inhibits the predictive
performance of machine learning algorithms. While many data-level and algorithm-level …

Crowdsourced data streams are popular and extremely valuable in several domains,
namely in tourism. Tourism crowdsourcing platforms rely on past tourist and business inputs …

当前大数据在多个领域广泛存在, 大数据的质量对其有效应用起着至关重要的作用,
因而需要对大数据进行质量管理. 尽管数据质量管理方面已经有一些研究成果 …

In this paper, we study the problem of learning from concept drifting data streams with noise,
where samples in a data stream may be mislabeled or contain erroneous values. Our …

Ensemble learning has become a common tool for data stream classification, being able to
handle large volumes of stream data and concept drifting. Previous studies focus on building …

Stream data are often dirty, for example, owing to unreliable sensor reading or erroneous
extraction of stock prices. Most stream data cleaning approaches employ a smoothing filter …

In this paper, we formulate a new research problem of concept learning and summarization
for one-class data streams. The main objectives are to (1) allow users to label instance …