Extending Propositional Satisfiability to Determine Minimal Fuzzy-Rough Reducts

Andrew Tuson, Qiang Shen, Richard Jensen

Research output: Contribution to conferencePaperpeer-review

2 Citations (SciVal)
256 Downloads (Pure)

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.
Original languageEnglish
Pages1415-1422
Number of pages8
Publication statusPublished - 2010

Fingerprint

Dive into the research topics of 'Extending Propositional Satisfiability to Determine Minimal Fuzzy-Rough Reducts'. Together they form a unique fingerprint.

Cite this