A relative decision entropy-based feature selection approach

DOI10.1016/j.patcog.2015.01.023zbMath1374.68391OpenAlexW2045810864MaRDI QIDQ1678710

Yuefei Sui, Lin Zhou, Feng Jiang

Publication date: 20 November 2017

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2015.01.023

zbMATH Keywords

rough setsroughnessfeature selectiondegree of dependencyfeature significancerelative decision entropy

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (12)

Three-way attribute reducts ⋮ Class-specific information measures and attribute reducts for hierarchy and systematicness ⋮ Efficient feature selection based on correlation measure between continuous and discrete features ⋮ A random approximate reduct-based ensemble learning approach and its application in software defect prediction ⋮ Three-way weighted combination-entropies based on three-layer granular structures ⋮ Feature selection based on double-hierarchical and multiplication-optimal fusion measurement in fuzzy neighborhood rough sets ⋮ Granular-conditional-entropy-based attribute reduction for partially labeled data with proxy labels ⋮ Efficient attribute reduction from the viewpoint of discernibility ⋮ Feature selection with partition differentiation entropy for large-scale data sets ⋮ Initialization of \(K\)-modes clustering using outlier detection techniques ⋮ Three-way class-specific attribute reducts from the information viewpoint ⋮ Ensemble learning based on approximate reducts and bootstrap sampling

Uses Software

• UCI-ml
• ROSETTA
• FSMRDE
• KDD Cup
• RSES
• RSL

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