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The introduction and utilization of ( l , u )‐graphs in the extended variable precision rough sets model
Author(s) -
Bey Malcolm J.
Publication year - 2003
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.10130
Subject(s) - rough set , categorical variable , decision table , graph , mathematics , computer science , data mining , variable (mathematics) , artificial intelligence , discrete mathematics , statistics , mathematical analysis
The extended variable precision rough sets model (VPRS l , u ) is a development of the rough set theory methodology first introduced in the seminal work by Zdzislaw Pawlak. As a technique for data analysis it is based on a set theoretical approach for the possible classification of groups of objects (condition classes) in a decision table to categorical decision attribute values (decision classes). Two measures are considered in this paper which utilize the l and u values necessary within VPRS l , u , the degree of dependency ( l , u )‐ DoD of a decision class and the quality of classification ( l , u )‐ QoC of objects in the model. This article introduces the notion of the ( l , u )‐graph, which elucidates the effect of the choice of the l and u values on the associated levels of ( l , u )‐ DoD and ( l , u )‐ QoC . A number of descriptive measures including specific lines are defined that utilize the information contained in the ( l , u )‐graphs. These measures and lines are used to intelligently identify and select subsets of condition attributes described ( l , u )‐reducts and a choice of the l and u values, based on retaining the underlying ( l , u )‐ DoD or ( l , u )‐ QoC within the associated ( l , u )‐graph. © 2003 Wiley Periodicals, Inc.