Concept Drift Detection Method for Noisy Big Data Streams based on Least Squares Line Fitting

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

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Publication Details

Journal
International Journal of Internet Protocol Technology
Published
2025-01-01
DOI
https://doi.org/10.1504/ijipt.2025.10072667
Primary Topic
Data Stream Mining Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Concept Drift Detection Method for Noisy Big Data Streams based on Least Squares Line Fitting

Zhanlei Shang, Fazhan Geng, Zhihong Qin
International Journal of Internet Protocol Technology
Data Stream Mining Techniques
article

Concept Drift Detection Method for Noisy Big Data Streams based on Least Squares Line Fitting

Zhanlei Shang, Fazhan Geng, Zhihong Qin
article en

Abstract

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

International Journal of Internet Protocol TechnologyVol. 18(3)
Climate action
Openalex Percentile: Top 91%
Data Stream Mining Techniques
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Concept Drift Detection Method for Noisy Big Data Streams based on Least Squares Line Fitting — Zhanlei Shang, Fazhan Geng, et al. · International Journal of Internet Protocol Technology (2025) | TGRS Research Map | TGRS