On the public citation graph

Adaptive concept drift detection

0 signed · 140 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1006/jmva.1994.1033openalex-snapshot
Two-Sample Test Statistics for Measuring Discrepancies Between Two Multivariate Probability Density Functions Using Kernel-Based Density Estimates
inferredcites →https://doi.org/10.1007/3-540-36175-8openalex-snapshot
Advances in Knowledge Discovery and Data Mining: 7th Pacific-Asia Conference, PAKDD 2003. Seoul, Korea, April 30 - May 2, 2003, Proceedings
inferredcites →https://doi.org/10.1007/978-1-4612-0711-5openalex-snapshot
A Probabilistic Theory of Pattern Recognition
inferredcites →https://doi.org/10.1007/978-3-540-68125-0_15openalex-snapshot
Unsupervised Change Analysis Using Supervised Learning
inferredcites →https://doi.org/10.1007/978-3-642-20212-4openalex-snapshot
Probability in Banach Spaces: Isoperimetry and Processes
inferredcites →https://doi.org/10.1007/b97823openalex-snapshot
Advances in Artificial Intelligence
inferredcites →https://doi.org/10.1007/bf00116900openalex-snapshot
Learning in the Presence of Concept Drift and Hidden Contexts
inferredcites →https://doi.org/10.1007/bf00993161openalex-snapshot
Tracking Drifting Concepts By Minimizing Disagreements
inferredcites →https://doi.org/10.1016/0012-365x(89)90299-9openalex-snapshot
Surveys in combinatorics 1987
inferredcites →https://doi.org/10.1080/00401706.1994.10485821openalex-snapshot
Detection of Abrupt Changes: Theory and Application
inferredcites →https://doi.org/10.1093/biomet/41.1-2.100openalex-snapshot
CONTINUOUS INSPECTION SCHEMES
inferredcites →https://doi.org/10.1093/biomet/89.2.359openalex-snapshot
Permutation tests for equality of distributions in high-dimensional settings
inferredcites →https://doi.org/10.1145/1281192.1281264openalex-snapshot
Statistical change detection for multi-dimensional data
inferredcites →https://doi.org/10.1145/1961189.1961199openalex-snapshot
LIBSVM
inferredcites →https://doi.org/10.1145/307400openalex-snapshot
Proceedings of the twelfth annual conference on Computational learning theory
inferredcites →https://doi.org/10.1145/502512.502570openalex-snapshot
Discovering outlier filtering rules from unlabeled data
inferredcites →https://doi.org/10.1214/aos/1176344722openalex-snapshot
Multivariate Generalizations of the Wald-Wolfowitz and Smirnov Two-Sample Tests
inferredcites →https://doi.org/10.3233/ida-2004-8305openalex-snapshot
Learning drifting concepts: Example selection vs. example weighting
inferredcites →https://doi.org/10.52202/085713openalex-snapshot
Advances in Neural Information Processing Systems 19
inferredcites →https://doi.org/10.7551/mitpress/11474.003.0014openalex-snapshot
Neural Information Processing Systems

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/asi.23452openalex-snapshot
A quantitative analysis of the temporal effects on automatic text classification
inferred← citeshttps://doi.org/10.1002/sam.11216openalex-snapshot
Fast and accurate detection of changes in data streams
inferred← citeshttps://doi.org/10.1002/sam.11669openalex-snapshot
Online learning for streaming data classification in nonstationary environments
inferred← citeshttps://doi.org/10.1002/widm.1327openalex-snapshot
No Free Lunch Theorem for concept drift detection in streaming data classification: A review
inferred← citeshttps://doi.org/10.1007/978-3-030-46150-8_39openalex-snapshot
Fast Likelihood-Based Change Point Detection
inferred← citeshttps://doi.org/10.1007/978-3-031-06433-3_8openalex-snapshot
Case Study on the Use of the SafeML Approach in Training Autonomous Driving Vehicles
inferred← citeshttps://doi.org/10.1007/978-3-319-00969-8_32openalex-snapshot
Comparable Study of Statistical Tests for Virtual Concept Drift Detection
inferred← citeshttps://doi.org/10.1007/978-3-319-14063-6_5openalex-snapshot
Subspace Detection on Concept Drifting Data Stream
inferred← citeshttps://doi.org/10.1007/978-3-319-25667-2_3openalex-snapshot
Handling Concept Drift
inferred← citeshttps://doi.org/10.1007/978-3-319-26555-1_45openalex-snapshot
Visual and Dynamic Change Detection for Data Streams
inferred← citeshttps://doi.org/10.1007/978-3-319-27926-8_15openalex-snapshot
An Adaptive Classification Framework for Unsupervised Model Updating in Nonstationary Environments
inferred← citeshttps://doi.org/10.1007/978-3-319-59162-9_51openalex-snapshot
Drift Detection Algorithm Using the Discriminant Function of the Base Classifiers
inferred← citeshttps://doi.org/10.1007/978-3-642-14274-1_16openalex-snapshot
Detecting Change via Competence Model
inferred← citeshttps://doi.org/10.1007/978-3-642-23863-5_60openalex-snapshot
Context Change Detection for Resource Allocation in Service-Oriented Systems
inferred← citeshttps://doi.org/10.1007/978-3-642-28931-6_53openalex-snapshot
Drift Detection and Model Selection Algorithms: Concept and Experimental Evaluation
inferred← citeshttps://doi.org/10.1007/978-3-642-32518-2_15openalex-snapshot
Enhancing Concept Drift Detection with Simulated Recurrence
inferred← citeshttps://doi.org/10.1007/978-3-642-33684-3_6openalex-snapshot
Learning and Transferring Geographically Weighted Regression Trees across Time
inferred← citeshttps://doi.org/10.1007/978-3-658-46162-1_3openalex-snapshot
Drifterkennung und –behandlung
inferred← citeshttps://doi.org/10.1007/978-3-662-44654-6_40openalex-snapshot
Identifying Features with Concept Drift in Multidimensional Data Using Statistical Tests
inferred← citeshttps://doi.org/10.1007/978-981-97-2650-9_13openalex-snapshot
Handling Concept Drift in Non-stationary Bandit Through Predicting Future Rewards
inferred← citeshttps://doi.org/10.1007/978-981-99-7007-0_3openalex-snapshot
Drift Detection and Handling
inferred← citeshttps://doi.org/10.1007/s00521-016-2564-5openalex-snapshot
A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks
inferred← citeshttps://doi.org/10.1007/s00521-020-05190-1openalex-snapshot
Aggregate density-based concept drift identification for dynamic sensor data models
inferred← citeshttps://doi.org/10.1007/s10115-021-01582-4openalex-snapshot
Data stream classification with novel class detection: a review, comparison and challenges
inferred← citeshttps://doi.org/10.1007/s10462-015-9444-8openalex-snapshot
Novelty detection in data streams
inferred← citeshttps://doi.org/10.1007/s10462-020-09939-xopenalex-snapshot
Concept learning using one-class classifiers for implicit drift detection in evolving data streams
inferred← citeshttps://doi.org/10.1007/s10489-018-1205-3openalex-snapshot
Parameter estimation in abruptly changing dynamic environments using stochastic learning weak estimator
inferred← citeshttps://doi.org/10.1007/s10618-015-0448-4openalex-snapshot
Characterizing concept drift
inferred← citeshttps://doi.org/10.1007/s10618-018-0554-1openalex-snapshot
Analyzing concept drift and shift from sample data
inferred← citeshttps://doi.org/10.1007/s11704-020-0206-7openalex-snapshot
Adaptive watermark generation mechanism based on time series prediction for stream processing
inferred← citeshttps://doi.org/10.1007/s12530-012-9067-0openalex-snapshot
EVE: a framework for event detection
inferred← citeshttps://doi.org/10.1007/s12530-016-9168-2openalex-snapshot
Discussion and review on evolving data streams and concept drift adapting
inferred← citeshttps://doi.org/10.1007/s12559-015-9341-0openalex-snapshot
Self-Adaptive Windowing Approach for Handling Complex Concept Drift
inferred← citeshttps://doi.org/10.1007/s13042-020-01159-7openalex-snapshot
Causative label flip attack detection with data complexity measures
inferred← citeshttps://doi.org/10.1007/s40747-019-00124-4openalex-snapshot
Data-driven decision support under concept drift in streamed big data
inferred← citeshttps://doi.org/10.1007/s42452-024-06097-5openalex-snapshot
Research on concept drift algorithm based on evolutionary computation
inferred← citeshttps://doi.org/10.1016/b978-0-443-31564-0.00006-1openalex-snapshot
Modeling and adaptation for dynamic industrial data
inferred← citeshttps://doi.org/10.1016/j.artint.2014.01.001openalex-snapshot
Concept drift detection via competence models
inferred← citeshttps://doi.org/10.1016/j.compeleceng.2016.09.006openalex-snapshot
Concept drift detection for data stream learning based on angle optimized global embedding and principal component analysis in sensor networks
inferred← citeshttps://doi.org/10.1016/j.compind.2016.09.004openalex-snapshot
Reconfiguration process for neuronal classification models: Application to a quality monitoring problem
inferred← citeshttps://doi.org/10.1016/j.engappai.2019.08.015openalex-snapshot
On utilizing weak estimators to achieve the online classification of data streams
inferred← citeshttps://doi.org/10.1016/j.eswa.2017.04.008openalex-snapshot
On the reliable detection of concept drift from streaming unlabeled data
inferred← citeshttps://doi.org/10.1016/j.future.2022.03.039openalex-snapshot
Towards automatic model specialization for edge video analytics
inferred← citeshttps://doi.org/10.1016/j.ifacol.2015.06.383openalex-snapshot
Estimating Processing Times within Context-aware Manufacturing Systems
inferred← citeshttps://doi.org/10.1016/j.inffus.2017.02.004openalex-snapshot
Ensemble learning for data stream analysis: A survey
inferred← citeshttps://doi.org/10.1016/j.inffus.2022.07.024openalex-snapshot
Non-IID data and Continual Learning processes in Federated Learning: A long road ahead
inferred← citeshttps://doi.org/10.1016/j.ins.2014.07.005openalex-snapshot
Adaptive ensembles for face recognition in changing video surveillance environments
inferred← citeshttps://doi.org/10.1016/j.ins.2019.08.050openalex-snapshot
Exploiting evolving micro-clusters for data stream classification with emerging class detection
inferred← citeshttps://doi.org/10.1016/j.is.2017.04.004openalex-snapshot
A Two-Stage Machine learning approach for temporally-robust text classification
inferred← citeshttps://doi.org/10.1016/j.jnca.2019.102479openalex-snapshot
An efficient reinforcement learning-based Botnet detection approach
inferred← citeshttps://doi.org/10.1016/j.knosys.2020.105792openalex-snapshot
An online Bayesian approach to change-point detection for categorical data
inferred← citeshttps://doi.org/10.1016/j.knosys.2022.108632openalex-snapshot
From concept drift to model degradation: An overview on performance-aware drift detectors
inferred← citeshttps://doi.org/10.1016/j.neucom.2010.06.037openalex-snapshot
A robust incremental learning method for non-stationary environments
inferred← citeshttps://doi.org/10.1016/j.neucom.2012.12.047openalex-snapshot
Intrinsic plasticity via natural gradient descent with application to drift compensation
inferred← citeshttps://doi.org/10.1016/j.neucom.2013.07.048openalex-snapshot
Customer profile classification: To adapt classifiers or to relabel customer profiles?
inferred← citeshttps://doi.org/10.1016/j.neucom.2026.134330openalex-snapshot
MCE-CDD: Concept drift detection based on multi-granularity conditional entropy
inferred← citeshttps://doi.org/10.1016/j.patcog.2016.05.001openalex-snapshot
Stochastic discretized learning-based weak estimation: a novel estimation method for non-stationary environments
inferred← citeshttps://doi.org/10.1016/j.patcog.2022.109113openalex-snapshot
Detecting group concept drift from multiple data streams
inferred← citeshttps://doi.org/10.1016/j.patrec.2013.02.005openalex-snapshot
RCD: A recurring concept drift framework
inferred← citeshttps://doi.org/10.1016/j.trpro.2017.05.173openalex-snapshot
Maritime vessel traffic modeling in the context of concept drift
inferred← citeshttps://doi.org/10.1080/10618600.2019.1617160openalex-snapshot
Anomaly Detection in Streaming Nonstationary Temporal Data
inferred← citeshttps://doi.org/10.1080/1206212x.2023.2289706openalex-snapshot
Machine learning in concept drift detection using statistical measures
inferred← citeshttps://doi.org/10.1080/24694452.2021.1985956openalex-snapshot
The Field Geomorphologist in a Time of Artificial Intelligence and Machine Learning
inferred← citeshttps://doi.org/10.1109/access.2017.2714345openalex-snapshot
Fast Learning and Testing for Imbalanced Multi-Class Changes in Streaming Data by Dynamic Multi-Stratum Network
inferred← citeshttps://doi.org/10.1109/cbi54897.2022.00012openalex-snapshot
No Time like the Present: Effects of Language Change on Automated Comment Moderation
inferred← citeshttps://doi.org/10.1109/cidue.2014.7007870openalex-snapshot
Statistical hypothesis testing for chemical detection in changing environments
inferred← citeshttps://doi.org/10.1109/cit.2016.34openalex-snapshot
Concept Drift Detection on Streaming Data under Limited Labeling
inferred← citeshttps://doi.org/10.1109/compsac54236.2022.00168openalex-snapshot
Concept drift detection for distributed multi-model machine learning systems
inferred← citeshttps://doi.org/10.1109/cybconf.2013.6617426openalex-snapshot
A rule-plus-exemplar classification system for adapting to concept growth
inferred← citeshttps://doi.org/10.1109/dsaa.2014.7058088openalex-snapshot
A probabilistic condensed representation of data for stream mining
inferred← citeshttps://doi.org/10.1109/dsaa.2015.7344814openalex-snapshot
Modeling recurrent distributions in streams using possible worlds
inferred← citeshttps://doi.org/10.1109/eais.2013.6604106openalex-snapshot
Learning imbalanced classes in the presence of concept growth
inferred← citeshttps://doi.org/10.1109/esem.2013.20openalex-snapshot
Learning from Open-Source Projects: An Empirical Study on Defect Prediction
inferred← citeshttps://doi.org/10.1109/iccke.2011.6413362openalex-snapshot
New ensemble method for classification of data streams
inferred← citeshttps://doi.org/10.1109/icdm.2012.89openalex-snapshot
Estimating the Expected Effectiveness of Text Classification Solutions under Subclass Distribution Shifts
inferred← citeshttps://doi.org/10.1109/icdmw.2011.173openalex-snapshot
Unifying Change -- Towards a Framework for Detecting the Unexpected
inferred← citeshttps://doi.org/10.1109/icdmw.2012.122openalex-snapshot
The PerfSim Algorithm for Concept Drift Detection in Imbalanced Data
inferred← citeshttps://doi.org/10.1109/icmla.2018.00058openalex-snapshot
Detecting Different Types of Concept Drifts with Ensemble Framework
inferred← citeshttps://doi.org/10.1109/icpr56361.2022.9956048openalex-snapshot
Online Adaptive Metrics for Model Evaluation on Non-representative Offline Test Data
inferred← citeshttps://doi.org/10.1109/ijcnn.2015.7280398openalex-snapshot
Concept drift detection for streaming data
inferred← citeshttps://doi.org/10.1109/ijcnn.2018.8489049openalex-snapshot
Adaptive Incremental Gaussian Mixture Network for Non-Stationary Data Stream Classification
inferred← citeshttps://doi.org/10.1109/ijcnn52387.2021.9533334openalex-snapshot
Concept Drift Detection via Boundary Shrinking
inferred← citeshttps://doi.org/10.1109/ijcnn52387.2021.9534387openalex-snapshot
Statistical Tests to Identify Virtual Concept Drifts
inferred← citeshttps://doi.org/10.1109/inm.2015.7140398openalex-snapshot
Analysis of the evolution of features in classification problems with concept drift: Application to spam detection
inferred← citeshttps://doi.org/10.1109/issrew53611.2021.00066openalex-snapshot
Methods for deep learning model failure detection and model adaption: A survey
inferred← citeshttps://doi.org/10.1109/iwcmc58020.2023.10182870openalex-snapshot
FLARE: Detection and Mitigation of Concept Drift for Federated Learning based IoT Deployments
inferred← citeshttps://doi.org/10.1109/jbhi.2018.2879805openalex-snapshot
An Adaptive, Data-Driven Personalized Advisor for Increasing Physical Activity
inferred← citeshttps://doi.org/10.1109/jiot.2021.3104289openalex-snapshot
Environment-Aware Dense Video Captioning for IoT-Enabled Edge Cameras
inferred← citeshttps://doi.org/10.1109/jsyst.2022.3179967openalex-snapshot
Sequentially Environment-Aware and Recursive Multiscene Image Enhancement for IoT-Enabled Smart Services
inferred← citeshttps://doi.org/10.1109/mci.2020.2998231openalex-snapshot
A Review of Off-Line Mode Dataset Shifts
inferred← citeshttps://doi.org/10.1109/med.2018.8442652openalex-snapshot
Efficient Tracking of Statistical Properties of Data Streams with Rapid Changes
inferred← citeshttps://doi.org/10.1109/pst.2011.5971980openalex-snapshot
Detecting P2P botnets through network behavior analysis and machine learning
inferred← citeshttps://doi.org/10.1109/saso.2012.13openalex-snapshot
Gossip-Based Learning under Drifting Concepts in Fully Distributed Networks
inferred← citeshttps://doi.org/10.1109/sisy.2012.6339511openalex-snapshot
Detecting concept drift in fully distributed environments
inferred← citeshttps://doi.org/10.1109/ssci47803.2020.9308146openalex-snapshot
A Change Detector for Prior Probabilities of Classes
inferred← citeshttps://doi.org/10.1109/tencon.2016.7847953openalex-snapshot
Pyramid stack data stream mining for handling concept-drifting
inferred← citeshttps://doi.org/10.1109/tkde.2017.2772239openalex-snapshot
Robust Prototype-Based Learning on Data Streams
inferred← citeshttps://doi.org/10.1109/tr.2016.2591504openalex-snapshot
Some Challenges and Opportunities in Reliability Engineering
inferred← citeshttps://doi.org/10.1111/exsy.12059openalex-snapshot
SCR: simulated concept recurrence – a non‐supervised tool for dealing with shifting concept
inferred← citeshttps://doi.org/10.1142/s0129065717500484openalex-snapshot
Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks
inferred← citeshttps://doi.org/10.1142/s0219525913500215openalex-snapshot
MASSIVELY DISTRIBUTED CONCEPT DRIFT HANDLING IN LARGE NETWORKS
inferred← citeshttps://doi.org/10.1145/3158645openalex-snapshot
Activity Recognition with Evolving Data Streams
inferred← citeshttps://doi.org/10.1145/3573942.3573979openalex-snapshot
A Concept Drift Detection Approach Based on Jensen-Shannon Divergence for Network Traffic Classification
inferred← citeshttps://doi.org/10.1145/3587098openalex-snapshot
Combining Diverse Meta-Features to Accurately Identify Recurring Concept Drift in Data Streams
inferred← citeshttps://doi.org/10.1145/3708497openalex-snapshot
Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach
inferred← citeshttps://doi.org/10.1155/2016/8091267openalex-snapshot
Adaptive Online Sequential ELM for Concept Drift Tackling
inferred← citeshttps://doi.org/10.1177/1748006x20946529openalex-snapshot
A method for fault diagnosis in evolving environment using unlabeled data
inferred← citeshttps://doi.org/10.1587/transinf.2017edp7091openalex-snapshot
An Efficient Concept Drift Detection Method for Streaming Data under Limited Labeling
inferred← citeshttps://doi.org/10.2478/jdis-2025-0020openalex-snapshot
Dynamic domain analysis for predicting concept drift in engineering AI-enabled software
inferred← citeshttps://doi.org/10.3233/idt-210115openalex-snapshot
Heuristic ensemble for unsupervised detection of multiple types of concept drift in data stream classification
inferred← citeshttps://doi.org/10.3233/idt-230065openalex-snapshot
Explainable data stream mining: Why the new models are better
inferred← citeshttps://doi.org/10.33042/2522-1809-2024-3-184-16-22openalex-snapshot
ADAPTATION OF THE ALGORITHM FOR DETECTING ANOMALIES IN TIME SERIES FOR NON-STATIONARY STREAMING DATA
inferred← citeshttps://doi.org/10.3384/ecp20176365openalex-snapshot
Adaptation framework for an industrial digital twin
inferred← citeshttps://doi.org/10.3389/frai.2022.955314openalex-snapshot
A survey on detecting healthcare concept drift in AI/ML models from a finance perspective
inferred← citeshttps://doi.org/10.3389/frai.2024.1330257openalex-snapshot
One or two things we know about concept drift—a survey on monitoring in evolving environments. Part A: detecting concept drift
inferred← citeshttps://doi.org/10.3390/e23030380openalex-snapshot
Non Stationary Multi-Armed Bandit: Empirical Evaluation of a New Concept Drift-Aware Algorithm
inferred← citeshttps://doi.org/10.3390/make7010002openalex-snapshot
Comparison of Off-the-Shelf Methods and a Hotelling Multidimensional Approximation for Data Drift Detection
inferred← citeshttps://doi.org/10.3390/s21248401openalex-snapshot
A Bibliometric Analysis and Benchmark of Machine Learning and AutoML in Crash Severity Prediction: The Case Study of Three Colombian Cities
inferred← citeshttps://doi.org/10.3390/smartcities7050107openalex-snapshot
SemConvTree: Semantic Convolutional Quadtrees for Multi-Scale Event Detection in Smart City
inferred← citeshttps://doi.org/10.36001/phme.2016.v3i1.1627openalex-snapshot
A Semi-Supervised Feature Selection Approach for Fault Diagnostics in Evolving Environments
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.