Results 21 to 30 of about 5,397,718 (323)
Handling Concept Drift for Predictions in Business Process Mining [PDF]
, 2020 Predictive services nowadays play an important role across all business sectors. However, deployed machine learning models are challenged by changing data streams over time which is described as concept drift.
Baier, Lucas +2 more
core +3 more sources
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
openaire +3 more sources
Concept Drift Adaptation with Incremental–Decremental SVM
Applied Sciences, 2021 Data classification in streams where the underlying distribution changes over time is known to be difficult. This problem—known as concept drift detection—involves two aspects: (i) detecting the concept drift and (ii) adapting the classifier.
Honorius Gâlmeanu, Răzvan Andonie
doaj +1 more source
Concept Drift Data Stream Classification Algorithm Based on McDiarmid Bound
Jisuanji kexue yu tansuo, 2021 Concept drift in data streams can cause significant performance degradation of existing classification models. Most current data stream algorithms for concept drift only aim at a certain type of concept drift (such as abrupt, gradual, or recurring drift),
LIANG Bin, LI Guanghui
doaj +1 more source
Detecting and Responding to Concept Drift in Business Processes
Algorithms, 2022 Concept drift, which refers to changes in the underlying process structure or customer behaviour over time, is inevitable in business processes, causing challenges in ensuring that the learned model is a proper representation of the new data.
Lingkai Yang +4 more
doaj +1 more source
Handling Concept Drift in Global Time Series Forecasting [PDF]
arXiv.org, 2023 Machine learning (ML) based time series forecasting models often require and assume certain degrees of stationarity in the data when producing forecasts.
Ziyi Liu +3 more
semanticscholar +1 more source
DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation [PDF]
AAAI Conference on Artificial Intelligence, 2022 In many real-world scenarios, we often deal with streaming data that is sequentially collected over time. Due to the non-stationary nature of the environment, the streaming data distribution may change in unpredictable ways, which is known as the concept
Wendi Li +4 more
semanticscholar +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
Mining frequent itemsets from streaming transaction data using genetic algorithms
Journal of Big Data, 2020 This paper presents a study of mining frequent itemsets from streaming data in the presence of concept drift. Streaming data, being volatile in nature, is particularly challenging to mine.
Sikha Bagui, Patrick Stanley
doaj +1 more source
A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams [PDF]
IEEE Internet of Things Magazine, 2021 In recent years, with the increasing popularity of “Smart Technology”, the number of Internet of Things (IoT) devices and systems have surged significantly.
Li Yang, A. Shami
semanticscholar +1 more source