In many real-world applications data arrive continuously, in the form of streams. Such data
can be used for the acquisition of knowledge by machine learning methods. In data streams
learning, novelty detection is a relevant topic, which aims to identify the emergence of a new
concept or a drift in the known concept in real time. Most approaches in the literature that
focus on the novelty detection problem, make assumptions that limit the method usefulness.
For instance, some methods are designed lying on the supposition that labeled data will be …

One of the main characteristics of Data Streams is the arrival of examples on a continuous
way, which can change over time. An important feature of these algorithms is novelty
detection, which makes it possible to identify new classes, which may arise, even if their true
labels are not yet available and update the decision model accordingly. Some approaches
await the arrival of the true labels of the examples already classified to use this information
in updating and maintaining the decision model. This time is called latency. Most …