Adaptive fuzzy multi-neighborhood feature selection with hybrid sampling and its application for class-imbalanced data-Reference-Cited by-同舟云学术

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Adaptive fuzzy multi-neighborhood feature selection with hybrid sampling and its application for class-imbalanced data

Published:2023-12 Issue: Volume:149 Page:110968
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Sun Lin^ORCID,Li Mengmeng,Ding Weiping^ORCID,Xu Jiucheng

Publisher

Elsevier BV

Subject

Software

Reference50 articles.

1. Deep instance envelope network-based imbalance learning algorithm with multilayer fuzzy C-means clustering and minimum interlayer discrepancy;Li;Appl. Soft Comput.,2022

2. Partial multilabel learning using fuzzy neighbourhood-based ball clustering and kernel extreme learning machine;Sun;IEEE Trans. Fuzzy Syst.,2023

3. TFSFB: Two-stage feature selection via fusing fuzzy multi-neighborhood rough set with binary whale optimization for imbalanced data;Sun;Inf. Fusion,2023

4. XGBoost for imbalanced data based on cost-sensitive activation function;Li;Chin. Comput. Sci.,2022

5. Multiobjective sparrow search feature selection with sparrow ranking and preference information and its applications for high-dimensional data;Sun;Appl. Soft Comput.,2023

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1. Fuzzy C-Means Clustering via Slime Mold and the Fisher Score;International Journal of Fuzzy Systems;2024-09-15

2. Fuzzy Neighborhood-Based Manifold Learning and Feature Weight Matrix for Multilabel Feature Selection;Knowledge-Based Systems;2024-09

3. CMEFS: chaotic mapping-based mayfly optimization with fuzzy entropy for feature selection;Applied Intelligence;2024-06-06

4. LSFSR: Local label correlation-based sparse multilabel feature selection with feature redundancy;Information Sciences;2024-05

5. Online streaming feature selection based on hierarchical structure information;Concurrency and Computation: Practice and Experience;2024-04-16

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