4.7 Article

Classification in Dynamic Data Streams With a Scarcity of Labels

Related references

Note: Only part of the references are listed.
Article Computer Science, Information Systems

Finding and Tracking Multi-Density Clusters in Online Dynamic Data Streams

Conor Fahy et al.

Summary: The proposed MDSC algorithm addresses the challenges of change in dynamic stream mining by using multiple density clustering and outlier buffering. Experimental results demonstrate its superior performance on a variety of real and synthetic data streams, showing good scalability and noise robustness.

IEEE TRANSACTIONS ON BIG DATA (2022)

Article Computer Science, Artificial Intelligence

Novelty Detection and Online Learning for Chunk Data Streams

Yi Wang et al.

Summary: The paper introduces an efficient framework for novelty detection and incremental learning for unlabeled chunk data streams by solving a linear system in the kernel space to generate Reproducing Kernel Hilbert Space (RKHS). Different approaches proposed based on this framework show good performance in novelty detection and online learning.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2021)

Article Computer Science, Information Systems

Exploiting evolving micro-clusters for data stream classification with emerging class detection

Salah Ud Din et al.

INFORMATION SCIENCES (2020)

Article Geochemistry & Geophysics

Class Boundary Exemplar Selection Based Incremental Learning for Automatic Target Recognition

Sihang Dang et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2020)

Article Automation & Control Systems

Ant Colony Stream Clustering: A Fast Density Clustering Algorithm for Dynamic Data Streams

Conor Fahy et al.

IEEE TRANSACTIONS ON CYBERNETICS (2019)

Article Computer Science, Artificial Intelligence

Concept-evolution detection in non-stationary data streams: a fuzzy clustering approach

Poorya ZareMoodi et al.

KNOWLEDGE AND INFORMATION SYSTEMS (2019)

Article Computer Science, Artificial Intelligence

Adapting dynamic classifier selection for concept drift

Paulo R. L. Almeida et al.

EXPERT SYSTEMS WITH APPLICATIONS (2018)

Article Computer Science, Artificial Intelligence

Detection of evolving concepts in non-stationary data streams: A multiple kernel learning approach

Sajjad Kamali Siahroudi et al.

EXPERT SYSTEMS WITH APPLICATIONS (2018)

Article Computer Science, Artificial Intelligence

Ensemble learning for data stream analysis: A survey

Bartosz Krawczyk et al.

INFORMATION FUSION (2017)

Article Computer Science, Information Systems

Fully online clustering of evolving data streams into arbitrarily shaped clusters

Richard Hyde et al.

INFORMATION SCIENCES (2017)

Article Computer Science, Artificial Intelligence

Adaptive random forests for evolving data stream classification

Heitor M. Gomes et al.

MACHINE LEARNING (2017)

Article Computer Science, Artificial Intelligence

A survey on data preprocessing for data stream mining: Current status and future directions

Sergio Ramirez-Gallego et al.

NEUROCOMPUTING (2017)

Article Computer Science, Artificial Intelligence

An ensemble of cluster-based classifiers for semi-supervised classification of non-stationary data streams

Mohammad Javad Hosseini et al.

KNOWLEDGE AND INFORMATION SYSTEMS (2016)

Article Computer Science, Hardware & Architecture

MuDi-Stream: A multi density clustering algorithm for evolving data stream

Amineh Amini et al.

JOURNAL OF NETWORK AND COMPUTER APPLICATIONS (2016)

Article Computer Science, Artificial Intelligence

Learning in Nonstationary Environments: A Survey

Gregory Ditzler et al.

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE (2015)

Article Computer Science, Artificial Intelligence

Active Learning With Drifting Streaming Data

Indre Zliobaite et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2014)

Article Computer Science, Artificial Intelligence

A single pass algorithm for clustering evolving data streams based on swarm intelligence

Agostino Forestiero et al.

DATA MINING AND KNOWLEDGE DISCOVERY (2013)

Article Computer Science, Artificial Intelligence

SVStream: A Support Vector-Based Algorithm for Clustering Data Streams

Chang-Dong Wang et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2013)

Article Computer Science, Artificial Intelligence

Drift detection using uncertainty distribution divergence

Patrick Lindstrom et al.

EVOLVING SYSTEMS (2013)

Article Computer Science, Interdisciplinary Applications

A Semi-supervised Ensemble Approach for Mining Data Streams

Jing Liu et al.

JOURNAL OF COMPUTERS (2013)

Article Computer Science, Artificial Intelligence

Facing the reality of data stream classification: coping with scarcity of labeled data

Mohammad M. Masud et al.

KNOWLEDGE AND INFORMATION SYSTEMS (2012)

Article Computer Science, Artificial Intelligence

Learning from concept drifting data streams with unlabeled data

Xindong Wu et al.

NEUROCOMPUTING (2012)

Article Computer Science, Artificial Intelligence

Incremental Learning of Concept Drift in Nonstationary Environments

Ryan Elwell et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS (2011)

Article Computer Science, Artificial Intelligence

The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift

Leandro L. Minku et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2010)

Article Computer Science, Artificial Intelligence

Minimum spanning tree based one-class classifier

Piotr Juszczak et al.

NEUROCOMPUTING (2009)

Article Computer Science, Information Systems

Stream Data Clustering Based on Grid Density and Attraction

Li Tu et al.

ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA (2009)

Article Computer Science, Information Systems

Density-Based Clustering of Data Streams at Multiple Resolutions

Li Wan et al.

ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA (2009)

Article Engineering, Industrial

The changepoint model for statistical process control

DM Hawkins et al.

JOURNAL OF QUALITY TECHNOLOGY (2003)