In many applications of information systems learning algorithms have to act in dynamic
environments where data are collected in the form of transient data streams. Compared to …

In a dynamic stream there is an assumption that the underlying process generating the
stream is non-stationary and that concepts within the stream will drift and change as the …

Supervised data stream learning depends on the incoming sample's true label to update a
classifier's model. In real life, obtaining the ground truth for each instance is a challenging …

Network Traffic Classification (NTC) has become an important feature in various network
management operations, eg, Quality of Service (QoS) provisioning and security services …

In practical applications, data stream classification faces significant challenges, such as high
cost of labeling instances and potential concept drifting. We present a new online active …

We propose a new active learning method for classification, which handles label noise
without relying on multiple oracles (ie, crowdsourcing). We propose a strategy that selects …

Semi-supervised learning targets the common situation where labeled data are scarce but
unlabeled data are abundant. It uses unlabeled data to help supervised learning tasks. In …

With the increase of unlabeled data in medical datasets, the labelling process becomes a
more costly task. Therefore, active learning provides a framework to reduce the amount the …

Aspect level sentiment analysis is a fine grained task in sentiment analysis which identifies
the product features from an opinionated piece of text and maps the sentiment towards each …

In many industrial applications, obtaining labeled observations is not straightforward as it
often requires the intervention of human experts or the use of expensive testing equipment …