Feature Selection for Multi-Label Learning Based on F-Neighborhood Rough Sets

Feature Selection for Multi-Label Learning Based on F-Neighborhood Rough Sets

Multi-label learning is often applied to handle complex decision tasks, and feature selection is its essential part. The relation of labels is always ignored or not enough to consider for both multi-label learning and its feature selection. To deal with the problem, F-neighborhood rough sets are emp...

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Bibliographic Details
Main Authors: Zhixuan Deng, Zhonglong Zheng, Dayong Deng, Tianxiang Wang, Yiran He, Dawei Zhang
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Rough sets
feature selection
multi-label learning
F-neighborhood rough sets
attribute significance matrix
Online Access:https://ieeexplore.ieee.org/document/9007716/

Internet

https://ieeexplore.ieee.org/document/9007716/

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