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Acquisition of hierarchy‐structured probabilistic decision tables and rules from data
Author(s) -
Ziarko Wojciech
Publication year - 2003
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/1468-0394.00255
Subject(s) - hierarchy , computer science , decision tree , probabilistic logic , rough set , decision table , data mining , set (abstract data type) , reduction (mathematics) , tree (set theory) , variable (mathematics) , decision tree model , analytical hierarchy , theoretical computer science , artificial intelligence , analytic hierarchy process , operations research , mathematics , programming language , mathematical analysis , geometry , economics , market economy
The paper is concerned with the creation of predictive models from data within the framework of the variable precision rough set model. It is focused on two aspects of the model derivation: computation of uncertain, in general, rules from information contained in probabilistic decision tables and forming hierarchies of decision tables with the objective of reduction or elimination of decision boundaries in the resulting classifiers. A new technique of creation of a linearly structured hierarchy of decision tables is introduced and compared to tree‐structured hierarchy. It is argued that the linearly structured hierarchy has significant advantages over tree‐structured hierarchy.