Attribute reduction with fuzzy rough self-information measures
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Publication:2666770
DOI10.1016/j.ins.2020.11.021zbMath1474.68367OpenAlexW3109271319MaRDI QIDQ2666770
Weiping Ding, Zehong Cao, Changzhong Wang, Yang Huang
Publication date: 23 November 2021
Published in: Information Sciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.ins.2020.11.021
Related Items (17)
Feature selection based on label distribution and fuzzy mutual information ⋮ Unsupervised attribute reduction for mixed data based on fuzzy rough sets ⋮ Automated granule discovery in continuous data for feature selection ⋮ Information-theoretic partially labeled heterogeneous feature selection based on neighborhood rough sets ⋮ Weak multi-label learning with missing labels via instance granular discrimination ⋮ AFNFS: adaptive fuzzy neighborhood-based feature selection with adaptive synthetic over-sampling for imbalanced data ⋮ Granular approximations: a novel statistical learning approach for handling data inconsistency with respect to a fuzzy relation ⋮ Three-hierarchical three-way decision models for conflict analysis: a qualitative improvement and a quantitative extension ⋮ Attribute reduction based on fusion information entropy ⋮ Granular-conditional-entropy-based attribute reduction for partially labeled data with proxy labels ⋮ New uncertainty measurement for categorical data based on fuzzy information structures: an application in attribute reduction ⋮ Matrix representation of the conditional entropy for incremental feature selection on multi-source data ⋮ Noise-aware and correlation analysis-based for fuzzy-rough feature selection ⋮ Incremental fuzzy rough sets based feature subset selection using fuzzy min-max neural network preprocessing ⋮ GLRM: logical pattern mining in the case of inconsistent data distribution based on multigranulation strategy ⋮ Pointwise mutual information sparsely embedded feature selection ⋮ Sequential 3WD-based local optimal scale selection in dynamic multi-scale decision information systems
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