Granular maximum decision entropy-based monotonic uncertainty measure for attribute reduction

DOI10.1016/j.ijar.2018.10.014zbMath1452.68210OpenAlexW2898363035MaRDI QIDQ1726314

Jiajun Wen, Zhihui Lai, Can Gao, Jie Zhou, Wai-Keung Wong

Publication date: 20 February 2019

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ijar.2018.10.014

zbMATH Keywords

rough set theoryattribute reductionknowledge granularityuncertainty measuremaximum decision

Mathematics Subject Classification ID

Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items (11)

Class-specific information measures and attribute reducts for hierarchy and systematicness ⋮ Information-theoretic measures of uncertainty for interval-set decision tables ⋮ On modeling ambiguity through entropy ⋮ Feature selection based on double-hierarchical and multiplication-optimal fusion measurement in fuzzy neighborhood rough sets ⋮ Measurement, modeling, reduction of decision-theoretic multigranulation fuzzy rough sets based on three-way decisions ⋮ Assessing potential of organizations with fuzzy entropy ⋮ Granular-conditional-entropy-based attribute reduction for partially labeled data with proxy labels ⋮ Uncertainty measures for interval set information tables based on interval \(\delta \)-similarity relation ⋮ Discernibility matrix based incremental feature selection on fused decision tables ⋮ Three-way approximate reduct based on information-theoretic measure ⋮ Parameterized maximum-entropy-based three-way approximate attribute reduction

Uses Software

• UCI-ml
• WEKA
• PRMLT

Cites Work

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