Machine learning-assisted structure annotation of natural products based on MS and NMR data
G Hu, M Qiu - Natural Product Reports, 2023 - pubs.rsc.org
Covering: up to March 2023Machine learning (ML) has emerged as a popular tool for
analyzing the structures of natural products (NPs). This review presents a summary of the …
analyzing the structures of natural products (NPs). This review presents a summary of the …
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 …
Dynamic model interpretation-guided online active learning scheme for real-time safety assessment
Chunk-level real-time safety assessment of dynamic systems is a critical component of
industrial processes, which is essential to prevent hazards and reduce the risk of injury or …
industrial processes, which is essential to prevent hazards and reduce the risk of injury or …
An active learning budget-based oversampling approach for partially labeled multi-class imbalanced data streams
Learning classification models from multi-class imbalanced data streams is a challenging
task in machine learning. Moreover, there is a common assumption that all instances are …
task in machine learning. Moreover, there is a common assumption that all instances are …
Data augmentation on-the-fly and active learning in data stream classification
K Malialis, D Papatheodoulou… - 2022 IEEE …, 2022 - ieeexplore.ieee.org
There is an emerging need for predictive models to be trained on-the-fly, since in numerous
machine learning applications data are arriving in an online fashion. A critical challenge …
machine learning applications data are arriving in an online fashion. A critical challenge …
Autoencoder-based anomaly detection in streaming data with incremental learning and concept drift adaptation
J Li, K Malialis, MM Polycarpou - 2023 International Joint …, 2023 - ieeexplore.ieee.org
In our digital universe nowadays, enormous amount of data are produced in a streaming
manner in a variety of application areas. These data are often unlabelled. In this case …
manner in a variety of application areas. These data are often unlabelled. In this case …
A hybrid active-passive approach to imbalanced nonstationary data stream classification
In real-world applications, the process generating the data might suffer from nonstationary
effects (eg, due to seasonality, faults affecting sensors or actuators, and changes in the …
effects (eg, due to seasonality, faults affecting sensors or actuators, and changes in the …
AOCBLS: A Novel Active and Online Learning System for ECG Arrhythmia Classification with Less Labeled Samples
Electrocardiogram (ECG) is a pivotal determinant of cardiac arrhythmia. In practice, ECG
data are often acquired as continuous unlabeled chunks with high labeling costs and severe …
data are often acquired as continuous unlabeled chunks with high labeling costs and severe …
Unsupervised unlearning of concept drift with autoencoders
Concept drift refers to a change in the data distribution affecting the data stream of future
samples. Consequently, learning models operating on the data stream might become …
samples. Consequently, learning models operating on the data stream might become …
CSAL: Cost sensitive active learning for multi-source drifting stream
Multi-source stream classification is a prominent real-world problem challenged by the
limited real labels and non-stationary environment. Despite growing research achievements …
limited real labels and non-stationary environment. Despite growing research achievements …