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research-article

Partial Multilabel Learning Using Fuzzy Neighborhood-Based Ball Clustering and Kernel Extreme Learning Machine

Authors:

Tianxiang Wang,

Jiucheng XuAuthors Info & Claims

IEEE Transactions on Fuzzy Systems, Volume 31, Issue 7

Pages 2277 - 2291

https://doi.org/10.1109/TFUZZ.2022.3222941

Published: 01 July 2023 Publication History

Abstract

Partial multilabel learning (PML) has attracted considerable interest from scholars. Most PML models construct objective functions and optimize target parameters, which add noise to the training process and results in a poor classification effect. In addition, feature correlation is limited to linear transformations while ignoring the complex relationships among features. In this study, we develop a novel PML model with fuzzy neighborhood-based ball clustering and kernel extreme learning machine (KELM). To reduce the interference from noise, ball <italic>k</italic>-means clustering is introduced to preprocess partial multilabel data and initialize ball clustering. A new ball clustering model based on fuzzy neighborhood is first designed to address partial multilabel systems. The particle-ball fusion strategy is developed to merge the particle balls reasonably, and the fuzzy membership function and label enhancement are studied for the subsequent training process. Then novel KELM with feature transformation matrix of training data is produced to analyze the nonlinear relationships among features. Finally, a nonsmooth convex objective function with the regression model is constructed to analyze the complex nonlinear relationships among features, and the optimal solutions of three objective parameters are solved by accelerated proximal gradient optimization. Experiments on 14 datasets reveal the effectiveness of the developed algorithm.

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Cited By

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https://dl.acm.org/doi/10.1109/TFUZZ.2024.3440575
Chen YDing WJu HHuang JYin T(2024)Cascaded Two-Stage Feature Clustering and Selection via Separability and Consistency in Fuzzy Decision SystemsIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2024.342096332:9(5320-5333)Online publication date: 1-Sep-2024
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Sun LMa YDing WXu J(2024)Sparse feature selection via local feature and high-order label correlationApplied Intelligence10.1007/s10489-023-05136-954:1(565-591)Online publication date: 1-Jan-2024
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cover image IEEE Transactions on Fuzzy Systems

IEEE Transactions on Fuzzy Systems Volume 31, Issue 7

July 2023

396 pages

ISSN:1063-6706

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1063-6706 © 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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Published: 01 July 2023

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Cited By

Sun LLiang HDing WXu J(2024)Granular Ball Fuzzy Neighborhood Rough Sets-Based Feature Selection via Multiobjective Mayfly OptimizationIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2024.344057532:11(6112-6124)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TFUZZ.2024.3440575
Chen YDing WJu HHuang JYin T(2024)Cascaded Two-Stage Feature Clustering and Selection via Separability and Consistency in Fuzzy Decision SystemsIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2024.342096332:9(5320-5333)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1109/TFUZZ.2024.3420963
Sun LMa YDing WXu J(2024)Sparse feature selection via local feature and high-order label correlationApplied Intelligence10.1007/s10489-023-05136-954:1(565-591)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s10489-023-05136-9
Sun LSi SDing WWang XXu J(2023)TFSFBInformation Fusion10.1016/j.inffus.2023.02.01695:C(91-108)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1016/j.inffus.2023.02.016
Mwangi INderu LMwangi RNjagi D(2023)Hybrid interpretable model using roughset theory and association rule mining to detect interaction terms in a generalized linear modelExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121092234:COnline publication date: 30-Dec-2023
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Zhou HLi WZhang CZhan T(2023)Dynamic maintenance of updating rough approximations in interval-valued ordered decision systemsApplied Intelligence10.1007/s10489-023-04655-953:19(22161-22178)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1007/s10489-023-04655-9

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