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EXTRACTING LAWS FROM DECISION TABLES: A ROUGH SET APPROACH
Author(s) -
Skowron A.
Publication year - 1995
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.1995.tb00039.x
Subject(s) - set (abstract data type) , rough set , law , computer science , artificial intelligence , mathematics , pattern recognition (psychology) , algorithm , political science , programming language
We present some methods, based on the rough set and Boolean reasoning approaches, for extracting laws from decision tables. First we discuss several procedures for decision rules synthesis from decision tables. Next we show how to apply some near‐to‐functional relations between data to data filtration. Two methods of searching for new classifiers (features) are described: searching for new classifiers in a given set of logical formulas, and searching for some functions approximating near‐to‐functional relations.