Incremental learning, online learning, and data stream learning are terms commonly
associated with learning algorithms that update their models given a continuous influx of …

Activity recognition aims to provide accurate and opportune information on people's
activities by leveraging sensory data available in today's sensory rich environments …

Class imbalance poses new challenges when it comes to classifying data streams. Many
algorithms recently proposed in the literature tackle this problem using a variety of data …

Class evolution, the phenomenon of class emergence and disappearance, is an important
research topic for data stream mining. All previous studies implicitly regard class evolution …

New class detection and effective model expansion are of great importance in incremental
data mining. In open incremental data environments, data often come with novel classes, eg …

This paper investigates an important problem in stream mining, ie, classification under
streaming emerging new classes or SENC. The SENC problem can be decomposed into …

In massive data analysis, data usually come in streams. In the last years, several studies
have investigated novelty detection in these data streams. Different approaches have been …

This article presents an industrial implementation of a virtual sensor in the process of fault
detection of an induction motor. An ensemble-learning soft-sensor is developed to detect …

Developing effective and efficient data stream classifiers is challenging for the machine
learning community because of the dynamic nature of data streams. As a result, many data …

Streaming classification with emerging new class is an important problem of great research
challenge and practical value. In many real applications, the task often needs to handle …