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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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FL-MalDrift: a federated learning framework for malware detection under local concept drift [PDF]
Scientific ReportsAndroid malware evolves continuously, inducing concept drift that erodes the accuracy of learned detectors a challenge intensified in federated learning (FL), where non-IID and asynchronously shifting client data can destabilize aggregation despite ...
Amit Patel +3 more
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Adversarial Drift Detection in Intrusion Detection System
Dianxin kexue, 2015 The recent intrusion detection systems based on machine learning generally assume that the intrusion traffic always satisfies stationary of statistics.However,this assumption is not always held when adversaries arbitrarily alter the distribution of ...
Yaguan Qian, Xiaohui Guan
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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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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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Adaptive Concept Drift Detection [PDF]
Proceedings of the 2009 SIAM International Conference on Data Mining, 2009 AbstractAn established method to detect concept drift in data streams is to perform statistical hypothesis testing on the multivariate data in the stream. The statistical theory offers rank‐based statistics for this task. However, these statistics depend on a fixed set of characteristics of the underlying distribution. Thus, they work well whenever the
Dries, Anton, Rückert, Ulrich
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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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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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Drift Detection Using Uncertainty Distribution Divergence [PDF]
2011 IEEE 11th International Conference on Data Mining Workshops, 2011 Concept drift is believed to be prevalent in most data gathered from naturally occurring processes and thus warrants research by the machine learning community. There are a myriad of approaches to concept drift handling which have been shown to handle concept drift with varying degrees of success.
Lindstrom, Patrick +2 more
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