Ensemble learning for data stream analysis: A survey
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 …
environments where data are collected in the form of transient data streams. Compared to …
Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature …
When put into practice in the real world, predictive maintenance presents a set of challenges
for fault detection and prognosis that are often overlooked in studies validated with data from …
for fault detection and prognosis that are often overlooked in studies validated with data from …
Revisiting class-incremental learning with pre-trained models: Generalizability and adaptivity are all you need
Class-incremental learning (CIL) aims to adapt to emerging new classes without forgetting
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …
Class-incremental learning: A survey
Deep models, eg, CNNs and Vision Transformers, have achieved impressive achievements
in many vision tasks in the closed world. However, novel classes emerge from time to time in …
in many vision tasks in the closed world. However, novel classes emerge from time to time in …
A model or 603 exemplars: Towards memory-efficient class-incremental learning
Real-world applications require the classification model to adapt to new classes without
forgetting old ones. Correspondingly, Class-Incremental Learning (CIL) aims to train a …
forgetting old ones. Correspondingly, Class-Incremental Learning (CIL) aims to train a …
A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework
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 …
algorithms recently proposed in the literature tackle this problem using a variety of data …
Machine learning for streaming data: state of the art, challenges, and opportunities
Incremental learning, online learning, and data stream learning are terms commonly
associated with learning algorithms that update their models given a continuous influx of …
associated with learning algorithms that update their models given a continuous influx of …
Characterizing concept drift
Most machine learning models are static, but the world is dynamic, and increasing online
deployment of learned models gives increasing urgency to the development of efficient and …
deployment of learned models gives increasing urgency to the development of efficient and …
Leveraging machine learning and big data for smart buildings: A comprehensive survey
Future buildings will offer new convenience, comfort, and efficiency possibilities to their
residents. Changes will occur to the way people live as technology involves people's lives …
residents. Changes will occur to the way people live as technology involves people's lives …
Discussion and review on evolving data streams and concept drift adapting
Recent advances in computational intelligent systems have focused on addressing complex
problems related to the dynamicity of the environments. In increasing number of real world …
problems related to the dynamicity of the environments. In increasing number of real world …