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Classification systems in dynamic environments: an overview

https://doi.org/10.1002/widm.1184
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What this work says

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

inferredcites →https://doi.org/10.1002/9780470748992openalex-snapshot
Kernel Methods for Remote Sensing Data Analysis
inferredcites →https://doi.org/10.1007/11811305_4openalex-snapshot
Learning with Local Drift Detection
inferredcites →https://doi.org/10.1007/978-3-540-25966-4_1openalex-snapshot
Classifier Ensembles for Changing Environments
inferredcites →https://doi.org/10.1007/978-3-540-28645-5_29openalex-snapshot
Learning with Drift Detection
inferredcites →https://doi.org/10.1007/978-3-540-68234-9_25openalex-snapshot
Conceptual Clustering and Its Application to Concept Drift and Novelty Detection
inferredcites →https://doi.org/10.1007/978-3-540-75488-6_27openalex-snapshot
Detecting Concept Drift Using Statistical Testing
inferredcites →https://doi.org/10.1007/bf00116900openalex-snapshot
Learning in the Presence of Concept Drift and Hidden Contexts
inferredcites →https://doi.org/10.1007/s10115-009-0206-2openalex-snapshot
Tracking recurring contexts using ensemble classifiers: an application to email filtering
inferredcites →https://doi.org/10.1007/s10462-015-9444-8openalex-snapshot
Novelty detection in data streams
inferredcites →https://doi.org/10.1007/s12530-012-9067-0openalex-snapshot
EVE: a framework for event detection
inferredcites →https://doi.org/10.1016/j.asoc.2006.10.002openalex-snapshot
Ensembling evidential k-nearest neighbor classifiers through multi-modal perturbation
inferredcites →https://doi.org/10.1016/j.inffus.2006.11.002openalex-snapshot
Dynamic integration of classifiers for handling concept drift
inferredcites →https://doi.org/10.1016/j.procs.2015.03.093openalex-snapshot
Identifying Concept-drift in Twitter Streams
inferredcites →https://doi.org/10.1016/j.sigpro.2003.07.018openalex-snapshot
Novelty detection: a review—part 1: statistical approaches
inferredcites →https://doi.org/10.1023/a:1007420529897openalex-snapshot
Extracting Hidden Context
inferredcites →https://doi.org/10.1023/a:1022859003006openalex-snapshot
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy
inferredcites →https://doi.org/10.1098/rsta.1900.0019openalex-snapshot
VII. On the association of attributes in statistics: with illustrations from the material of the childhood society, &c
inferredcites →https://doi.org/10.1109/fskd.2012.6234061openalex-snapshot
Detecting and adapting to drifting concepts
inferredcites →https://doi.org/10.1109/iciip.2013.6707652openalex-snapshot
A novel online ensemble approach for concept drift in data streams
inferredcites →https://doi.org/10.1109/icpr.2004.1334060openalex-snapshot
Optimizing nearest neighbour in random subspaces using a multi-objective genetic algorithm
inferredcites →https://doi.org/10.1109/igarss.2012.6351935openalex-snapshot
Semi-supervised and unsupervised novelty detection using nested support vector machines
inferredcites →https://doi.org/10.1109/ijcnn.2007.4371035openalex-snapshot
The Importance of Diversity in Neural Network Ensembles - An Empirical Investigation
inferredcites →https://doi.org/10.1109/ijcnn.2007.4371129openalex-snapshot
Neural Network Ensembles for Time Series Prediction
inferredcites →https://doi.org/10.1109/ijcnn.2011.6033518openalex-snapshot
Multiple distribution data description learning method for novelty detection
inferredcites →https://doi.org/10.1109/tgrs.2010.2045764openalex-snapshot
Semisupervised One-Class Support Vector Machines for Classification of Remote Sensing Data
inferredcites →https://doi.org/10.1109/tkde.2011.58openalex-snapshot
DDD: A New Ensemble Approach for Dealing with Concept Drift
inferredcites →https://doi.org/10.1109/tkde.2012.136openalex-snapshot
Incremental Learning of Concept Drift from Streaming Imbalanced Data
inferredcites →https://doi.org/10.1109/tkde.2012.172openalex-snapshot
Predictive Handling of Asynchronous Concept Drifts in Distributed Environments
inferredcites →https://doi.org/10.1109/tnnls.2013.2251352openalex-snapshot
Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm
inferredcites →https://doi.org/10.1109/tnnls.2013.2271915openalex-snapshot
Mining Recurring Concepts in a Dynamic Feature Space
inferredcites →https://doi.org/10.1109/tnnls.2013.2278313openalex-snapshot
Dealing With Concept Drifts in Process Mining
inferredcites →https://doi.org/10.1145/1363686.1363912openalex-snapshot
Cluster-based novel concept detection in data streams applied to intrusion detection in computer networks
inferredcites →https://doi.org/10.1145/1401890.1401987openalex-snapshot
Categorizing and mining concept drifting data streams
inferredcites →https://doi.org/10.1145/1456650.1456651openalex-snapshot
Data fusion
inferredcites →https://doi.org/10.1145/1541880.1541882openalex-snapshot
Anomaly detection
inferredcites →https://doi.org/10.1145/2523813openalex-snapshot
A survey on concept drift adaptation
inferredcites →https://doi.org/10.1145/502512.502565openalex-snapshot
Experimental comparisons of online and batch versions of bagging and boosting
inferredcites →https://doi.org/10.1145/502512.502568openalex-snapshot
A streaming ensemble algorithm (SEA) for large-scale classification
inferredcites →https://doi.org/10.1145/956750.956778openalex-snapshot
Mining concept-drifting data streams using ensemble classifiers
inferredcites →https://doi.org/10.1201/chdaminodisopenalex-snapshot
Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
inferredcites →https://doi.org/10.1201/ebk1439826119openalex-snapshot
Knowledge Discovery from Data Streams
inferredcites →https://doi.org/10.5555/1314498.1390333openalex-snapshot
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts

What points at this work

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

inferred← citeshttps://doi.org/10.1002/widm.1318openalex-snapshot
Benchmarking in classification and regression
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/10.1007/978-3-030-98741-1_11openalex-snapshot
Ensemble Method for Online Sentiment Classification Using Drift Detection-Based Adaptive Window Method
inferred← citeshttps://doi.org/10.1007/s10618-019-00656-wopenalex-snapshot
A drift detection method based on dynamic classifier selection
inferred← citeshttps://doi.org/10.1016/j.neucom.2025.131667openalex-snapshot
CLEAR-E: Concept-aware lightweight energy adaptation for smart grid load forecasting
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/eurosp51992.2021.00033openalex-snapshot
Remote Non-Intrusive Malware Detection for PLCs based on Chain of Trust Rooted in Hardware
inferred← citeshttps://doi.org/10.1109/mis.2017.3711649openalex-snapshot
Challenges of Sentiment Analysis for Dynamic Events
inferred← citeshttps://doi.org/10.1109/msec.2018.2875369openalex-snapshot
The Need for Speed: An Analysis of Brazilian Malware Classifiers
inferred← citeshttps://doi.org/10.1201/9781315156026-20openalex-snapshot
Mining Ubiquitous Data Streams for IoT
inferred← citeshttps://doi.org/10.1201/9781315156026-25openalex-snapshot
Mining Ubiquitous Data Streams for IoT
inferred← citeshttps://doi.org/10.18297/etd/3947openalex-snapshot
Solving the challenges of concept drift in data stream classification.
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/10.32604/cmc.2023.035221openalex-snapshot
Drift Detection Method Using Distance Measures and Windowing Schemes for Sentiment Classification
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