Data stream classification with novel class detection: a review, comparison and challenges
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 …
learning community because of the dynamic nature of data streams. As a result, many data …
Anomalous example detection in deep learning: A survey
Deep Learning (DL) is vulnerable to out-of-distribution and adversarial examples resulting in
incorrect outputs. To make DL more robust, several posthoc (or runtime) anomaly detection …
incorrect outputs. To make DL more robust, several posthoc (or runtime) anomaly detection …
A Systematic Literature Review of Novelty Detection in Data Streams: Challenges and Opportunities
JG Gaudreault, P Branco - ACM Computing Surveys, 2024 - dl.acm.org
Novelty detection in data streams is the task of detecting concepts that were not known prior,
in streams of data. Many machine learning algorithms have been proposed to detect these …
in streams of data. Many machine learning algorithms have been proposed to detect these …
Smotefuna: Synthetic minority over-sampling technique based on furthest neighbour algorithm
Class imbalance occurs in classification problems in which the “normal” cases, or instances,
significantly outnumber the “abnormal” instances. Training a standard classifier on …
significantly outnumber the “abnormal” instances. Training a standard classifier on …
[PDF][PDF] Anomalous instance detection in deep learning: A survey
Deep Learning (DL) is vulnerable to out-of-distribution and adversarial examples resulting in
incorrect outputs. To make DL more robust, several posthoc anomaly detection techniques …
incorrect outputs. To make DL more robust, several posthoc anomaly detection techniques …
A reliable adaptive prototype-based learning for evolving data streams with limited labels
Data stream mining presents notable challenges in the form of concept drift and evolution.
Existing learning algorithms, typically designed within a supervised learning framework …
Existing learning algorithms, typically designed within a supervised learning framework …
Falsification detection system for IoV using randomized search optimization ensemble algorithm
Falsification detection is a critical advance in ensuring that real-time information about
vehicles and their movement states is certified on the Internet of Vehicles (IoV). Thus …
vehicles and their movement states is certified on the Internet of Vehicles (IoV). Thus …
A novel semi-supervised classification approach for evolving data streams
G Liao, P Zhang, H Yin, X Deng, Y Li, H Zhou… - Expert Systems with …, 2023 - Elsevier
Classification plays a crucial role in mining the evolving data streams. The concept drift and
concept evolution are the major issues of data streams classification, which greatly affect the …
concept evolution are the major issues of data streams classification, which greatly affect the …
Explainable predictive maintenance
Explainable Artificial Intelligence (XAI) fills the role of a critical interface fostering interactions
between sophisticated intelligent systems and diverse individuals, including data scientists …
between sophisticated intelligent systems and diverse individuals, including data scientists …
Predictive data mining techniques for fault diagnosis of electric equipment: A review
A Contreras-Valdes, JP Amezquita-Sanchez… - Applied Sciences, 2020 - mdpi.com
Data mining is a technological and scientific field that, over the years, has been gaining
more importance in many areas, attracting scientists, developers, and researchers around …
more importance in many areas, attracting scientists, developers, and researchers around …