Despite the availability and ease of collecting a large amount of free, unlabeled data, the
expensive and time-consuming labeling process is still an obstacle to labeling a sufficient …

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

In learning to classify streaming data, obtaining true labels may require major effort and may
incur excessive cost. Active learning focuses on carefully selecting as few labeled instances …

Classifiers deployed in the real world operate in a dynamic environment, where the data
distribution can change over time. These changes, referred to as concept drift, can cause the …

Data stream mining has become an important research area over the past decade due to the
increasing amount of data available today. Sources from various domains generate a near …

We have witnessed in recent years an ever-growing volume of information becoming
available in a streaming manner in various application areas. As a result, there is an …

Learning from data streams (incremental learning) is increasingly attracting research focus
due to many real-world streaming problems and due to many open challenges, among …

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 …

The evolving nature and accumulating volume of real-world data inevitably give rise to the
so-called “concept drift” issue, causing many deployed Case-Based Reasoning (CBR) …

A trained ML model is deployed on anothertest'dataset where target feature values (labels)
are unknown. Drift is distribution change between the training and deployment data, which is …