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Label distribution feature selection based on neighborhood rough set

Concurrency and Computation: Practice and Experience
SummaryIn label distribution learning (LDL), an instance is involved with many labels in different importance degrees, and the feature space of instances is accompanied with thousands of redundant and/or irrelevant features. Therefore, the main characteristic of feature selection in LDL is to evaluate the ability of each feature.
Yilin Wu 0001, Wenzhong Guo, Yaojin Lin
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Feature Selection Using Fuzzy Neighborhood Entropy-Based Uncertainty Measures for Fuzzy Neighborhood Multigranulation Rough Sets

IEEE Transactions on Fuzzy Systems, 2021
Lin Sun, Lanying Wang, Weiping Ding
exaly

GBRS: A Unified Granular-Ball Learning Model of Pawlak Rough Set and Neighborhood Rough Set

IEEE Transactions on Neural Networks and Learning Systems
Pawlak rough set (PRS) and neighborhood rough set (NRS) are the two most common rough set theoretical models. Although the PRS can use equivalence classes to represent knowledge, it is unable to process continuous data. On the other hand, NRSs, which can process continuous data, rather lose the ability of using equivalence classes to represent ...
Shuyin Xia +7 more
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A novel approach to attribute reduction based on weighted neighborhood rough sets

Knowledge-Based Systems, 2021
Eric C C Tsang, Yanting Guo, Degang Chen +2 more
exaly

A Framework for Feature Construction Based on Neighborhood Rough Set