Improving the β-Precision and OWA Based Fuzzy Rough Set Models: Definitions, Properties and Robustness Analysis

Since the early 1990s, many authors have studied fuzzy rough set models and their application in machine learning and data reduction. In this work, we adjust the β-precision and the ordered weighted average based fuzzy rough set models in such a way that the number of theoretical properties increase...

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Bibliographic Details
Published inRough Sets and Current Trends in Computing pp. 23 - 34
Main Authors D’eer, Lynn, Verbiest, Nele
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing 2014
SeriesLecture Notes in Computer Science
Subjects
beta-precision
conjunction
fuzzy sets
hybridization
implication
lower and upper approximation
OWA
robustness
rough sets
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Summary:Since the early 1990s, many authors have studied fuzzy rough set models and their application in machine learning and data reduction. In this work, we adjust the β-precision and the ordered weighted average based fuzzy rough set models in such a way that the number of theoretical properties increases. Furthermore, we evaluate the robustness of the new models a-β-PREC and a-OWA to noisy data and compare them to a general implicator-conjunctor-based fuzzy rough set model.
ISBN:331908643X
9783319086439
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-08644-6_3