Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare
Abstract Machine learning (ML) and artificial intelligence (AI) algorithms have the potential to
derive insights from clinical data and improve patient outcomes. However, these highly …
derive insights from clinical data and improve patient outcomes. However, these highly …
A review of domain adaptation without target labels
Domain adaptation has become a prominent problem setting in machine learning and
related fields. This review asks the question: How can a classifier learn from a source …
related fields. This review asks the question: How can a classifier learn from a source …
An introduction to domain adaptation and transfer learning
In machine learning, if the training data is an unbiased sample of an underlying distribution,
then the learned classification function will make accurate predictions for new samples …
then the learned classification function will make accurate predictions for new samples …
Learning in nonstationary environments: A survey
The prevalence of mobile phones, the internet-of-things technology, and networks of
sensors has led to an enormous and ever increasing amount of data that are now more …
sensors has led to an enormous and ever increasing amount of data that are now more …
Discussion and review on evolving data streams and concept drift adapting
I Khamassi, M Sayed-Mouchaweh, M Hammami… - Evolving systems, 2018 - Springer
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 …
On the reliable detection of concept drift from streaming unlabeled data
TS Sethi, M Kantardzic - Expert Systems with Applications, 2017 - Elsevier
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 …
distribution can change over time. These changes, referred to as concept drift, can cause the …
Online active learning ensemble framework for drifted data streams
J Shan, H Zhang, W Liu, Q Liu - IEEE transactions on neural …, 2018 - ieeexplore.ieee.org
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 …
cost of labeling instances and potential concept drifting. We present a new online active …
One or two things we know about concept drift—a survey on monitoring in evolving environments. Part A: detecting concept drift
The world surrounding us is subject to constant change. These changes, frequently
described as concept drift, influence many industrial and technical processes. As they can …
described as concept drift, influence many industrial and technical processes. As they can …
Evolving ensemble fuzzy classifier
The concept of ensemble learning offers a promising avenue in learning from data streams
under complex environments because it better addresses the bias and variance dilemma …
under complex environments because it better addresses the bias and variance dilemma …
On the robustness of field calibration for smart air quality monitors
The robustness of field calibrated Air Quality Multi-sensors (AQM) performances to long term
and/or mobile operation is still debated. Though accuracy generally exceeds the one of …
and/or mobile operation is still debated. Though accuracy generally exceeds the one of …