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Regularized robust fuzzy least squares twin support vector machine for class imbalance learning paper by Independent / Community
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Paper

Regularized robust fuzzy least squares twin support vector machine for class imbalance learning

by Independent / Community 001f207cc7a80ad08cdbab2b8eca23b0a8618cb3
Free2AITools Nexus Index
67.4
S: Semantic 50

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A: Authority 80
P: Popularity 56
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

Twin support vector machines (TWSVM) have been successfully applied to the classification problems. TWSVM is computationally efficient model of support vector machines (SVM). However, in real world classification problems issues of class imbalance and noise provide great challenges. Due to this, models lead to the inaccurate classification either due to higher tendency towards the majority class or due to the presence of noise. We provide an improved version of robust fuzzy least squares twin...

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Semantic Scholar 22 Citations
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Registry ID 001f207cc7a80ad08cdbab2b8eca23b0a8618cb3
License ArXiv
Provider semantic_scholar
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Cite this paper

Academic & Research Attribution

BibTeX 
@misc{001f207cc7a80ad08cdbab2b8eca23b0a8618cb3,
  author = {Unknown},
  title = {Regularized robust fuzzy least squares twin support vector machine for class imbalance learning Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/001f207cc7a80ad08cdbab2b8eca23b0a8618cb3}},
  note = {Accessed via Free2AITools.}
}
APA Style
Unknown. (2026). Regularized robust fuzzy least squares twin support vector machine for class imbalance learning [Paper]. Free2AITools. https://api.semanticscholar.org/001f207cc7a80ad08cdbab2b8eca23b0a8618cb3

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Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 80
Popularity (P) 56
Recency (R) 100
Quality (Q) 65

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FNI V2.0 for Regularized robust fuzzy least squares twin support vector machine for class imbalance learning: Authority (A:80), Popularity (P:56), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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πŸ“ Executive Summary

"Twin support vector machines (TWSVM) have been successfully applied to the classification problems. TWSVM is computationally efficient model of support vector machines (SVM). However, in real world classification problems issues of class imbalance and noise provide great challenges. Due to this, models lead to the inaccurate classification either due to higher tendency towards the majority class or due to the presence of noise. We provide an improved version of robust fuzzy least squares twin..."

❝ Cite Node 

@article{Unknown2026Regularized,
  title={Regularized robust fuzzy least squares twin support vector machine for class imbalance learning},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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πŸ“Š Research Signals

πŸ“ˆ22CitationsSemantic Scholar
πŸ›οΈ80AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
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🏷️ Research Topics

vector databases
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author
Unknown
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ArXiv
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