A drift detection method based on dynamic classifier selection
Sign Up to like & getrecommendations! Published in 2019 at "Data Mining and Knowledge Discovery"
DOI: 10.1007/s10618-019-00656-w
Abstract: Machine learning algorithms can be applied to several practical problems, such as spam, fraud and intrusion detection, and customer preferences, among others. In most of these problems, data come in streams, which mean that data… read more here.
Keywords: drift; detection; drift detection; method ... See more keywords
Unsupervised concept drift detection based on multi-scale slide windows
Sign Up to like & getrecommendations! Published in 2021 at "Ad Hoc Networks"
DOI: 10.1016/j.adhoc.2020.102325
Abstract: Abstract In the past few decades, research related to concept drift learning has been increasing, and many concept drift learning algorithms have also been developed and applied to actual data stream processing. In general, concept… read more here.
Keywords: concept drift; drift; drift detection; multi scale ... See more keywords
Concept drift detection in toxicology datasets using discriminative subgraph-based drift detector
Sign Up to like & getrecommendations! Published in 2022 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbac506
Abstract: Due to the increasing importance of graphs and graph streams in data representation in today's era, concept drift detection in graph streaming scenarios is more important than ever. Contributions to concept drift detection in graph… read more here.
Keywords: toxicology; drift detection; graph; concept drift ... See more keywords
A Novel Online and Non-Parametric Approach for Drift Detection in Big Data
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Access"
DOI: 10.1109/access.2017.2735378
Abstract: A sizable amount of current literature on online drift detection tools thrive on unrealistic parametric strictures such as normality or on non-parametric methods whose power performance is questionable. Using minimal realistic assumptions such as unimodality,… read more here.
Keywords: novel online; drift detection; non parametric; online non ... See more keywords
Multilayer Concept Drift Detection Method Based on Model Explainability
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Access"
DOI: 10.1109/access.2024.3517697
Abstract: Timely detection of concept drift plays a vital role in ensuring the stability and reliability of data-driven models. However, existing concept drift detection methods face challenges in achieving a proper balance between accuracy and timeliness… read more here.
Keywords: detection; drift; drift detection; concept drift ... See more keywords
An Integrated Preprocessing and Drift Detection Approach With Adaptive Windowing for Fraud Detection in Payment Systems
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Access"
DOI: 10.1109/access.2025.3569609
Abstract: As fraudulent transaction methods evolve rapidly; it becomes progressively more challenging to detect them in payment systems. Static machine learning and rule-based traditional detection methods cannot capture all the dynamic and evolving nature of fraudulent… read more here.
Keywords: detection; fraud detection; drift; drift detection ... See more keywords
Drift Detection for Black-Box Deep Learning Models
Sign Up to like & getrecommendations! Published in 2024 at "IT Professional"
DOI: 10.1109/mitp.2023.3338007
Abstract: Dataset drift is a common challenge in machine learning, especially for models trained on unstructured data, such as images. In this article, we propose a new approach for the detection of data drift in black-box… read more here.
Keywords: detection; drift; drift detection; black box ... See more keywords
Evaluation of Drift Detection Algorithms in the Condition Monitoring Domain
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2024.3452208
Abstract: In condition monitoring, early detection of process signal drifts indicating, e.g., equipment degradation is crucial. exponentially weighted moving average (EWMA), cumulative sum (CUSUM), and discrete average block (DAB)-based drift detectors are statistical and commonly used… read more here.
Keywords: detection; drift; drift detection; condition monitoring ... See more keywords
Hierarchical Reduced-Space Drift Detection Framework for Multivariate Supervised Data Streams
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2021.3111756
Abstract: In a streaming environment, the characteristics of the data themselves and their relationship with the labels may change over time. Most drift detection methods for supervised data streams are performance-based, that is, they detect changes… read more here.
Keywords: data streams; drift detection; space; supervised data ... See more keywords
Application of concept drift detection and adaptive framework for non linear time series data from cardiac surgery
Sign Up to like & getrecommendations! Published in 2024 at "Computational Intelligence"
DOI: 10.1111/coin.12658
Abstract: The quality of machine learning (ML) models deployed in dynamic environments tends to decline over time due to disparities between the data used for training and the upcoming data available for prediction, which is commonly… read more here.
Keywords: drift; drift detection; time; adaptive framework ... See more keywords
PGraphD*: Methods for Drift Detection and Localisation Using Deep Learning Modelling of Business Processes
Sign Up to like & getrecommendations! Published in 2022 at "Entropy"
DOI: 10.3390/e24070910
Abstract: This paper presents a set of methods, jointly called PGraphD*, which includes two new methods (PGraphDD-QM and PGraphDD-SS) for drift detection and one new method (PGraphDL) for drift localisation in business processes. The methods are… read more here.
Keywords: drift detection; localisation; detection; deep learning ... See more keywords