An Experimental Analysis of Drift Detection Methods on Multi-Class Imbalanced Data Streams
Applied Sciences, 2022 The performance of machine learning models diminishes while predicting the Remaining Useful Life (RUL) of the equipment or fault prediction due to the issue of concept drift.
Abdul Sattar Palli +4 more
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Empirical data drift detection experiments on real-world medical imaging data [PDF]
Nature CommunicationsWhile it is common to monitor deployed clinical artificial intelligence (AI) models for performance degradation, it is less common for the input data to be monitored for data drift – systemic changes to input distributions.
Ali Kore +6 more
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A brute force tuning of training length for concept drift
Frontiers in Physics, 2023 We present a brute-force approach to analyze the concept drift behind time sequence data. This approach, named SELECT, searches for the optimal length of training data to minimize error metrics.
Takumi Uchida +2 more
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Design of adaptive ensemble classifier for online sentiment analysis and opinion mining [PDF]
PeerJ Computer Science, 2021 DataStream mining is a challenging task for researchers because of the change in data distribution during classification, known as concept drift. Drift detection algorithms emphasize detecting the drift.
Sanjeev Kumar +3 more
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Anomalies Detection Using Isolation in Concept-Drifting Data Streams
Computers, 2021 Detecting anomalies in streaming data is an important issue for many application domains, such as cybersecurity, natural disasters, or bank frauds. Different approaches have been designed in order to detect anomalies: statistics-based, isolation-based ...
Maurras Ulbricht Togbe +5 more
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Trace2Vec-CDD: A Framework for Concept Drift Detection in Business Process Logs using Trace Embedding [PDF]
Computer and Knowledge Engineering, 2023 Business processes are subject to changes during their execution over time due to new legislation, seasonal effects, and so on. Detection of process changes is alternatively called business process drift detection. Currently, existing methods unfavorably
Fatemeh Khojasteh +3 more
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Drift-Aware Monocular Localization Based on a Pre-Constructed Dense 3D Map in Indoor Environments
ISPRS International Journal of Geo-Information, 2018 Recently, monocular localization has attracted increased attention due to its application to indoor navigation and augmented reality. In this paper, a drift-aware monocular localization system that performs global and local localization is presented ...
Guanyuan Feng +3 more
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Survey of Concept Drift Handling Methods in Data Streams [PDF]
Jisuanji kexue, 2022 At present,concept drift in the nonstationary data stream presents a trend of different speeds and and different space distribution,which has brought great challenges to many fields such as data mining and machine learning.In the past two de-cades,many ...
CHEN Zhi-qiang, HAN Meng, LI Mu-hang, WU Hong-xin, ZHANG Xi-long
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Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives
Future Internet, 2023 The stream mining paradigm has become increasingly popular due to the vast number of algorithms and methodologies it provides to address the current challenges of Internet of Things (IoT) and modern machine learning systems.
Guilherme Yukio Sakurai +3 more
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Feature Drift in Fake News Detection: An Interpretable Analysis
Applied Sciences, 2023 In recent years, fake news detection and its characteristics have attracted a number of researchers. However, most detection algorithms are driven by data rather than theories, which causes the existing approaches to only perform well on specific ...
Chenbo Fu +5 more
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