Title :
Extending propositional satisfiability to determine minimal fuzzy-rough reducts
Author :
Jensen, Richard ; Tuson, Andrew ; Shen, Qiang
Author_Institution :
Dept. of Comput. Sci., Aberystwyth Univ., Aberystwyth, UK
Abstract :
This paper describes a novel, principled approach to real-valued dataset reduction based on fuzzy and rough set theory. The approach is based on the formulation of fuzzy-rough discernibility matrices, that can be transformed into a satisfiability problem; an extension of rough set approaches that only apply to discrete datasets. The fuzzy-rough hybrid reduction method is then realised algorithmically by a modified version of a traditional satisifability approach. This produces an efficient and provably optimal approach to data reduction that works well on a number of machine learning benchmarks in terms of both time and classification accuracy.
Keywords :
computability; data reduction; fuzzy set theory; learning (artificial intelligence); matrix algebra; pattern classification; rough set theory; data reduction; discrete datasets; fuzzy set theory; fuzzy-rough discernibility matrix; fuzzy-rough hybrid reduction method; machine learning benchmark; minimal fuzzy-rough reduct; propositional satisfiability; real-valued dataset reduction; rough set theory; satisfiability problem; Approximation methods; Equations; Facsimile; Information systems; Machine learning algorithms; Rough sets; Symmetric matrices;
Conference_Titel :
Fuzzy Systems (FUZZ), 2010 IEEE International Conference on
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-6919-2
DOI :
10.1109/FUZZY.2010.5584470