An incremental attribute reduction method for dynamic data mining

DOI10.1016/j.ins.2018.07.001zbMath1441.68207OpenAlexW2831672891WikidataQ57558505 ScholiaQ57558505MaRDI QIDQ2200538

Ni Cheng, Baoli Wang, Tianrui Li, Yunge Jing, Hamido Fujita

Publication date: 22 September 2020

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2018.07.001

zbMATH Keywords

incremental learningattribute reductiondecision systemknowledge granularity

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items

Feature selection based on double-hierarchical and multiplication-optimal fusion measurement in fuzzy neighborhood rough sets ⋮ A unified incremental updating framework of attribute reduction for two-dimensionally time-evolving data ⋮ Matrix representation of the conditional entropy for incremental feature selection on multi-source data ⋮ A dynamic rule-based classification model via granular computing ⋮ Efficient approaches for maintaining dominance-based multigranulation approximations with incremental granular structures ⋮ Local logical disjunction double-quantitative rough sets ⋮ Discernibility matrix based incremental feature selection on fused decision tables ⋮ Incremental updating probabilistic neighborhood three-way regions with time-evolving attributes ⋮ Incremental updating probabilistic approximations under multi-level and multi-dimensional variations in hybrid incomplete decision systems

Cites Work

• A fuzzy rough set approach for incremental feature selection on hybrid information systems
• An incremental attribute reduction approach based on knowledge granularity under the attribute generalization
• Rough sets based matrix approaches with dynamic attribute variation in set-valued information systems
• Rough set approach to incomplete numerical data
• Dynamic maintenance of approximations in set-valued ordered decision systems under the attribute generalization
• Relative reducts in consistent and inconsistent decision tables of the Pawlak rough set model
• Rudiments of rough sets
• Gaussian kernel based fuzzy rough sets: model, uncertainty measures and applications
• Positive approximation: an accelerator for attribute reduction in rough set theory
• Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation
• Approaches to knowledge reduction based on variable precision rough set model.
• Rough set models in multigranulation spaces
• Efficient parallel Boolean matrix based algorithms for computing composite rough set approximations
• Efficient updating rough approximations with multi-dimensional variation of ordered data
• On the rough consistency measures of logic theories and approximate reasoning in rough logic
• Updating attribute reduction in incomplete decision systems with the variation of attribute set
• Rough set methods in feature selection and recognition
• THE INFORMATION ENTROPY, ROUGH ENTROPY AND KNOWLEDGE GRANULATION IN ROUGH SET THEORY
• Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing
• Unnamed Item
• Unnamed Item
• Unnamed Item