Results 281 to 290 of about 5,397,718 (323)

Learning Under Concept Drift

2021
Most extant machine learning strategies focus on learning to make predictions in environments that assume concepts never change. This thesis studies how algorithms can make effective predictions in a changing world where processes of interest are constantly evolving.
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Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift

Neural Information Processing Systems
Data heterogeneity is one of the key challenges in federated learning, and many efforts have been devoted to tackling this problem. However, distributed concept drift with data heterogeneity, where clients may additionally experience different concept ...
Junbao Chen, Jingfeng Xue, Yong Wang, Zhenyan Liu, Lu Huang   +4 more
semanticscholar   +1 more source

Addressing Concept Drift in IoT Anomaly Detection: Drift Detection, Interpretation, and Adaptation

IEEE Transactions on Sustainable Computing
Anomaly detection plays a vital role as a crucial security measure for edge devices in Artificial Intelligence and Internet of Things (AIoT). With the rapid development of IoT (Internet of Things), changes in system configurations and the introduction of
Lijuan Xu, Ziyu Han, Dawei Zhao, Xin Li, Fuqiang Yu, Chuan Chen   +5 more
semanticscholar   +1 more source

ReCDA: Concept Drift Adaptation with Representation Enhancement for Network Intrusion Detection

Knowledge Discovery and Data Mining
The deployment of learning-based models to detect malicious activities in network traffic flows is significantly challenged by concept drift. With evolving attack technology and dynamic attack behaviors, the underlying data distribution of recently ...
Shuo Yang, Xinran Zheng, Jinze Li, Jinfeng Xu, Xingjun Wang, Edith C. H. Ngai   +5 more
semanticscholar   +1 more source

Concept Drift

2005
Traditional approaches to data mining are based on an assumption that the process that generated or is generating a data stream is static. Although this assumption holds for many applications, it does not hold for many others. Consider systems that build models for identifying important e-mail.
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Dealing With Concept Drifts in Process Mining

IEEE Transactions on Neural Networks and Learning Systems, 2014
Although most business processes change over time, contemporary process mining techniques tend to analyze these processes as if they are in a steady state. Processes may change suddenly or gradually. The drift may be periodic (e.g., because of seasonal influences) or one-of-a-kind (e.g., the effects of new legislation).
Jagadeesh Chandra Bose, R.P., Aalst, van der, W.M.P., Zliobaite, I., Pechenizkiy, M.   +3 more
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Adaptation to Drifting Concepts

2003
Most of supervised learning algorithms assume the stability of the target concept over time. Nevertheless in many real-user modeling systems, where the data is collected over an extended period of time, the learning task can be complicated by changes in the distribution underlying the data.
Gladys Castillo, João Gama, Pedro Medas
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Handling Concept Drift

2015
In this chapter, the different methods and techniques used to learn from data streams in evolving and nonstationary environments will be presented, and their performances will be compared according to the generated drift characteristics as well as to the application context and objectives.
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Paired Learners for Concept Drift

2008 Eighth IEEE International Conference on Data Mining, 2008
To cope with concept drift, we paired a stable online learner with a reactive one. A stable learner predicts based on all of its experience, whereas are active learner predicts based on its experience over a short, recent window of time. The method of paired learning uses differences in accuracy between the two learners over this window to determine ...
Stephen H. Bach, Marcus A. Maloof
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Concept Signatures and Semantic Drift

2011
Ontology evolution is the process of incrementally and consistently adapting an existing ontology to changes in the relevant domain. Semantic drift refers to how ontology concepts’ intentions gradually change as the domain evolves. Normally, a semantic drift captures small domain changes that are hard to detect with traditional ontology management ...
Jon Atle Gulla, Geir Solskinnsbakk, Per Myrseth, Veronika Haderlein, Olga Cerrato   +4 more
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