Reducts within the variable precision rough sets model: A further investigation

By Malcolm John Beynon. European Journal of Operational Research, 2001

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Granular Computing and Knowledge Reduction in Formal Contexts (2009, methodology): “Many types of attribute reduction have been proposed in the analysis of information systems and decision tables [5], [15], [22], [24], [33], [38], [53], [54], [64], [67], [68], each of 1462 IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL.”
Computational Intelligence and Feature Selection - Rough and Fuzzy Approaches (2008, background): “Extended classification of reducts in the VPRS approach may be found in [27,28,189].”
Dimensionality reduction based on rough set theory: A review (2009, methodology): “Beynon [9] Feature selection Presented an investigation of the criteria for a b-reduct within variable precision rough set (VPRS) model Galvez et al.”
Information-preserving hybrid data reduction based on fuzzy-rough techniques (2006, methodology): “And these techniques are applied to data reduction (Beynon, 2001; Li et al., 2004) text classification (Moradi et al., 1998), texture analysis (Swiniarski and Hargis, 2001).”
Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches (2004, background): “Consistency is defined as the number of discernible objects out of the entire object set—exactly that of the dependency measure.”
Attribute reduction in decision-theoretic rough set models (2008, methodology): “Different algorithms, approaches and methodologies have been extensively studied [1,36,2,4,11,13,17,20,29,35,41,51].”