Results 61 to 70 of about 336,769 (240)

Dual Self-Attention is What You Need for Model Drift Detection in 6G Networks

IEEE Transactions on Machine Learning in Communications and Networking
The advent of 6G networks heralds a transformative shift in communication technology, with Artificial Intelligence (AI) and Machine Learning (ML) forming the backbone of its architecture and operations.
Mazene Ameur, Bouziane Brik, Adlen Ksentini   +2 more
doaj   +1 more source

Intrusion Detection based on Concept Drift Detection & Online Incremental Learning [PDF]

, 2023
Farah Jemili, Khaled Jouini, Ouajdi Korbaa   +2 more
openalex   +1 more source

A Classification and Novel Class Detection Algorithm for Concept Drift Data Stream Based on the Cohesiveness and Separation Index of Mahalanobis Distance

Journal of Electrical and Computer Engineering, 2020
Data stream mining has become a research hotspot in data mining and has attracted the attention of many scholars. However, the traditional data stream mining technology still has some problems to be solved in dealing with concept drift and concept ...
Xiangjun Li, Yong Zhou, Ziyan Jin, Peng Yu, Shun Zhou   +4 more
doaj   +1 more source

Querying Temporal Drifts at Multiple Granularities [PDF]

, 2015
There exists a large body of work on online drift detection with the goal of dynamically finding and maintaining changes in data streams. In this paper, we adopt a query-based approach to drift detection. Our approach relies on a drift index, a structure
Amer-Yahia, Sihem, Christophides, Vassilis, Douzal-Chouakria, Ahlame, Kleisarchaki, Sofia   +3 more
core   +2 more sources

ADDAEIL: Anomaly Detection with Drift-Aware Ensemble-Based Incremental Learning

Algorithms
Time series anomaly detection in streaming environments faces persistent challenges due to concept drift, which gradually degrades model reliability.
Danlei Li, Nirmal-Kumar C. Nair, Kevin I-Kai Wang   +2 more
doaj   +1 more source

Underwater Multi-Robot Convoying using Visual Tracking by Detection

, 2017
We present a robust multi-robot convoying approach that relies on visual detection of the leading agent, thus enabling target following in unstructured 3-D environments.
Chang, Wei-Di, Dudek, Gregory, Henderson, Peter, Higuera, Juan Camilo Gamboa, Islam, Md Jahidul, Li, Jimmy, Manderson, Travis, Sattar, Junaed, Shkurti, Florian, Xu, Anqi   +9 more
core   +1 more source

Unsupervised Concept Drift Detection with a Discriminative Classifier [PDF]

Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019
In data stream mining, one of the biggest challenges is to develop algorithms that deal with the changing data. As data evolve over time, static models become outdated. This phenomenon is called concept drift, and it is investigated extensively in the literature.
Gozuacik, Omer, Buyukcakir, Alican, Bonab, Hamed, Can, Fazli   +3 more
openaire   +3 more sources

Improving the Performance of the Feature Drift Detector by Lasso Observation of Sample Feature Fluctuations

International Journal of Applied Mathematics and Computer Science
Feature drift is a subtype of data distribution drift that occurs when the statistical significance of input features changes over time, despite the overall decision boundary remaining stable.
Porwik Piotr, Orczyk Tomasz, Japkowicz Nathalie   +2 more
doaj   +1 more source

Concept Drift Analysis Based on Isolation Forest for Effectively Detecting Network Attack in IoT Scenarios

IEEE Access
In the Internet of Things era, massive network data streams are continuously generated, often exhibiting unpredictable distributional evolution. This dynamic nature poses significant challenges for effective analysis.
Renjie Chu, Mengzhen Luo, Lei Yang, Jianming Xiao, Yuanyuan Liao   +4 more
doaj   +1 more source

Adaptive Buffering Strategies for Incremental Learning Under Concept Drift in Lifestyle Disease Modeling

IEEE Access
Lifestyle diseases such as diabetes manifest through subtle and non-stationary clinical patterns, posing significant challenges for real-time prediction and monitoring.
B. S. Prashanth, M. V. Manoj Kumar, B. H. Puneetha, Nasser Abdo Saif Almuraqab, Ariful Hoque, Immanuel Azaad Moonesar, Ananth Rao   +6 more
doaj   +1 more source