Recently, stream data mining applications has drawn vital attention from several research
communities. Stream data is continuous form of data which is distinguished by its online …

Recently, stream data mining applications has drawn vital attention from several research
communities. Stream data is continuous form of data which is distinguished by its online …

The processing of streaming data implies new requirements concerning restrictive
processing time, limited amount of memory and one scan of incoming instances. One of the …

With the wide application of machine learning algorithms to the real world, class imbalance
and concept drift have become crucial learning issues. Applications in various domains such …

In recent years, data stream has been considered as one of the primary sources of big data.
Data stream has grown very rapidly in the last decades. Data stream environment has many …

Abstract Concept drifts occurring in data streams will jeopardize the accuracy and stability of
the online learning process. If the data stream is imbalanced, it will be even more …

The primary focus of machine learning has traditionally been on learning from data assumed
to be sufficient and representative of the underlying fixed, yet unknown, distribution. Such …

Learning from data streams with both online class imbalance and concept drift (OCI-CD) is
receiving much attention in today's world. Due to this problem, the performance is affected …

Mining data streams has recently been the subject of extensive research efforts. However,
most of the works conducted in this field assume a balanced class distribution underlying …

With unlimited growth of real-world data size and increasing requirement of real-time
processing, immediate processing of big stream data has become an urgent problem. In …