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Combining similarity in time and space for training set formation under concept drift [PDF]
, 2011 Concept drift is a challenge in supervised learning for sequential data. It describes a phenomenon when the data distributions change over time. In such a case accuracy of a classifier benefits from the selective sampling for training.
Zliobaite, I Indré +2 more
core +1 more source
Mathematics, 2023 Data stream mining deals with processing large amounts of data in nonstationary environments, where the relationship between the data and the labels often changes.
Muhammad Zafran Muhammad Zaly Shah +4 more
doaj +1 more source
CoRR, 2017 Concept drift is a major issue that greatly affects the accuracy and reliability of many real-world applications of machine learning. We argue that to tackle concept drift it is important to develop the capacity to describe and analyze it. We propose tools for this purpose, arguing for the importance of quantitative descriptions of drift in marginal ...
Geoffrey I. Webb +3 more
openaire +3 more sources
CONDA-PM—A Systematic Review and Framework for Concept Drift Analysis in Process Mining
Algorithms, 2020 Business processes evolve over time to adapt to changing business environments. This requires continuous monitoring of business processes to gain insights into whether they conform to the intended design or deviate from it.
Ghada Elkhawaga +4 more
doaj +1 more source
Rapidrift: Elementary Techniques to Improve Machine Learning-Based Malware Detection
Computers, 2023 Artificial intelligence and machine learning have become a necessary part of modern living along with the increased adoption of new computational devices.
Abishek Manikandaraja +2 more
doaj +1 more source
An Ensemble Extreme Learning Machine for Data Stream Classification
Algorithms, 2018 Extreme learning machine (ELM) is a single hidden layer feedforward neural network (SLFN). Because ELM has a fast speed for classification, it is widely applied in data stream classification tasks.
Rui Yang, Shuliang Xu, Lin Feng
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DED: Drift Principle in Educational Evolved Data
Tikrit Journal of Pure Science, 2022 Clustering data streams is one of the prominent tasks of discovering hidden patterns in data streams. It refers to the process of clustering newly arrived data into continuously and dynamically changing segmentation patterns.
Ammar Thaher Yaseen Al Abd Alazeez
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Counterfactual Explanations of Concept Drift
CoRR, 2020 The notion of concept drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time; as a consequence machine learning models may become inaccurate and need adjustment. While there do exist methods to detect concept drift or to adjust models in the presence of observed drift, the question of explaining ...
Fabian Hinder, Barbara Hammer
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Learning in the presence of concept recurrence in data stream clustering
Journal of Big Data, 2020 In the case of real-world data streams, the underlying data distribution will not be static; it is subject to variation over time, which is known as the primary reason for concept drift.
K. Namitha, G. Santhosh Kumar
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Adaptive Incremental-Learning Ensemble Classification Approach for Concept Drift Problem
Jisuanji kexue yu tansuo, 2020 The performance of the machine learning model always decreases with the occurrence of concept drift due to the non-stationary characteristics of the data flow.
HAN Mingming, SUN Guanglu, ZHU Suxia
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