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An Overview of Real World Applications with Concept Drifting Data Streams

https://doi.org/10.2139/ssrn.3170324
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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/rob.20279openalex-extracted
Learning terrain segmentation with classifier ensembles for autonomous robot navigation in unstructured environments
inferredcites →https://doi.org/10.1007/11871842_4openalex-extracted
Winning the DARPA Grand Challenge
inferredcites →https://doi.org/10.1007/3-540-56602-3_139openalex-extracted
Effective learning in dynamic environments by explicit context tracking
inferredcites →https://doi.org/10.1007/978-3-540-39907-0_49openalex-extracted
Drifting Concepts as Hidden Factors in Clinical Studies
inferredcites →https://doi.org/10.1007/978-3-540-72079-9_2openalex-extracted
User Profiles for Personalized Information Access
inferredcites →https://doi.org/10.1007/978-3-642-01307-2_34openalex-extracted
A Multi-partition Multi-chunk Ensemble Technique to Classify Concept-Drifting Data Streams
inferredcites →https://doi.org/10.1007/s10618-006-0050-xopenalex-extracted
Mining in Anticipation for Concept Change: Proactive-Reactive Prediction in Data Streams
inferredcites →https://doi.org/10.1016/j.eswa.2009.03.031openalex-extracted
Designing an expert system for fraud detection in private telecommunications networks
inferredcites →https://doi.org/10.1016/j.fss.2004.03.028openalex-extracted
A methodology for dynamic data mining based on fuzzy clustering
inferredcites →https://doi.org/10.1023/a:1007420529897openalex-extracted
Extracting Hidden Context
inferredcites →https://doi.org/10.1109/msr.2009.5069480openalex-extracted
Tracking concept drift of software projects using defect prediction quality
inferredcites →https://doi.org/10.1109/tkde.2005.48openalex-extracted
Relevant data expansion for learning concept drift from sparsely labeled data
inferredcites →https://doi.org/10.1109/tsmca.2009.2025137openalex-extracted
Keeping the Resident in the Loop: Adapting the Smart Home to the User
inferredcites →https://doi.org/10.1145/1143844.1143859openalex-extracted
Dynamic topic models
inferredcites →https://doi.org/10.1145/1401890.1401970openalex-extracted
A bayesian mixture model with linear regression mixing proportions
inferredcites →https://doi.org/10.1145/1601966openalex-extracted
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
inferredcites →https://doi.org/10.1145/2818567.2818582openalex-extracted
Analyzing Delhi Assembly Election 2015 Using Textual Content of Social Network
inferredcites →https://doi.org/10.1145/288090.288122openalex-extracted
Temporal sequence learning and data reduction for anomaly detection
inferredcites →https://doi.org/10.1145/322510.322526openalex-extracted
Temporal sequence learning and data reduction for anomaly detection
inferredcites →https://doi.org/10.1145/775060.775061openalex-extracted
Bursty and hierarchical structure in streams
inferredcites →https://doi.org/10.3233/978-1-58603-891-5-763openalex-extracted
An Ensemble of Classifiers for coping with Recurring Contexts in Data Streams
inferredcites →https://doi.org/10.4018/ijirr.2017100101openalex-extracted
Offline vs. Online Sentiment Analysis
inferredcites →https://doi.org/10.4018/ijitwe.2017100101openalex-extracted
Real-Time Unspecified Major Sub-Events Detection in the Twitter Data Stream That Cause the Change in the Sentiment Score of the Targeted Event
inferredcites →https://doi.org/10.4304/jcp.2.10.76-88openalex-extracted
Direct and Indirect Human Computer Interaction Based Biometrics
inferredcites →https://doi.org/10.48550/arxiv.1010.4784openalex-extracted
Learning under Concept Drift: an Overview
inferredcites →https://doi.org/10.5120/20614-3280openalex-extracted
Online Methods of Learning in Occurrence of Concept Drift
inferredcites →https://doi.org/10.5815/ijisa.2016.12.08openalex-extracted
Empirical Study of Impact of Various Concept Drifts in Data Stream Mining Methods

What points at this work

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

inferred← citeshttps://doi.org/10.1007/978-981-13-2254-9_15openalex-snapshot
Challenges in Mining Big Data Streams
inferred← citeshttps://doi.org/10.24251/hicss.2021.120openalex-snapshot
Switching Scheme: A Novel Approach for Handling Incremental Concept Drift in Real-World Data Sets
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