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An ensemble machine learning approach through effective feature extraction to classify fake news

Research article (Future Generation Computer Systems, 2020) · cited 418× · AI/ML
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An ensemble machine learning approach through effective feature extraction to classify fake news

Summary

An ensemble machine learning approach through effective feature extraction to classify fake news is a scholarly article[1].

Key Facts

  • An ensemble machine learning approach through effective feature extraction to classify fake news's instance of is recorded as scholarly article[2].

Related Entities

Mamoun Alazab

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  1. [2] ↑ . wikidata.org.

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  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). An ensemble machine learning approach through effective feature extraction to classify fake news. Retrieved May 24, 2026, from https://4ort.xyz/entity/an-ensemble-machine-learning-approach-through-effective-feature-extraction-to-classify-fake-news
MLA “An ensemble machine learning approach through effective feature extraction to classify fake news.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/an-ensemble-machine-learning-approach-through-effective-feature-extraction-to-classify-fake-news.
BibTeX @misc{4ortxyz_an-ensemble-machine-learning-approach-through-effective-feature-extraction-to-classify-fake-news_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{An ensemble machine learning approach through effective feature extraction to classify fake news}}, year = {2026}, url = {https://4ort.xyz/entity/an-ensemble-machine-learning-approach-through-effective-feature-extraction-to-classify-fake-news}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): An ensemble machine learning approach through effective feature extraction to classify fake news — https://4ort.xyz/entity/an-ensemble-machine-learning-approach-through-effective-feature-extraction-to-classify-fake-news (retrieved 2026-05-24)

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