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Partial Covers, Reducts and Decision Rules in Rough Sets - Theory and Applications (Hardcover, 2008 ed.)
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Partial Covers, Reducts and Decision Rules in Rough Sets - Theory and Applications (Hardcover, 2008 ed.)
Series: Studies in Computational Intelligence, 145
Expected to ship within 10 - 15 working days
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This monograph is devoted to theoretical and experimental study of
partial
reductsandpartialdecisionrulesonthebasisofthestudyofpartialcovers.
The use of partial (approximate) reducts and decision rules instead
of exact ones
allowsustoobtainmorecompactdescriptionofknowledgecontainedindecision
tables, andtodesignmorepreciseclassi?ers.
Weconsideralgorithmsforconstructionofpartialreductsandpartialdecision
rules, boundsonminimalcomplexityofpartialreductsanddecisionrules,
and algorithms for construction of the set of all partial reducts
and the set of all irreducible partial decision rules. We discuss
results of numerous experiments with randomly generated and
real-life decision tables. These results show that partial reducts
and decision rules can be used in data mining and knowledge
discoverybothforknowledgerepresentationandforprediction.
Theresultsobtainedinthe monographcanbe usefulforresearchersinsuch
areasasmachinelearning, dataminingandknowledgediscovery,
especiallyfor thosewhoareworkinginroughsettheory,
testtheoryandLAD(LogicalAnalysis ofData). The monographcan be
usedunder the creationofcoursesforgraduates- dentsandforPh. D.
studies. An essential part of software used in experiments will be
accessible soon in
RSES-RoughSetExplorationSystem(InstituteofMathematics, WarsawU-
versity, headofproject-ProfessorAndrzejSkowron). We are greatly
indebted to Professor Andrzej Skowron for stimulated d- cussionsand
varioussupportof ourwork. We aregratefulto ProfessorJanusz
Kacprzykforhelpfulsuggestions. Sosnowiec, Poland MikhailJu. Moshkov
April2008 MarcinPiliszczuk BeataZielosko Contents Introduction. . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . 1 1 PartialCovers,
ReductsandDecisionRules . . . . . . . . . . . . . . . . 7 1. 1
PartialCovers. . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . 8 1. 1. 1 MainNotions. . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1. 1. 2 Known Results. . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 9 1. 1. 3
PolynomialApproximateAlgorithms. . . . . . . . . . . . . . . . . .
10 1. 1. 4 Bounds on C (?)Based on Information about min
GreedyAlgorithm Work. . . . . . . . . . . . . . . . . . . . . . . .
. . . . . 13 1. 1. 5 UpperBoundon C (?). . . . . . . . . . . . . .
. . . . . . . . . . . . 17 greedy 1. 1. 6 Covers
fortheMostPartofSetCoverProblems. . . . . . . . 18 1. 2
PartialTests and Reducts. . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 22 1. 2. 1 MainNotions. . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . 22 1. 2.
2Relationships betweenPartialCovers and Partial Tests. . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . 23 1. 2. 3 PrecisionofGreedyAlgorithm. . . . . . . . . . .
. . . . . . . . . . . . 24 1. 2. 4 PolynomialApproximateAlgorithms.
. . . . . . . . . . . . . . . . . 25 1. 2. 5 Bounds on R (?)Based
on Information about min GreedyAlgorithm Work. . . . . . . . . . .
. . . . . . . . . . . . . . . . . . 26 1. 2. 6 UpperBoundon R (?).
. . . . . . . . . . . . . . . . . . . . . . . . . 28 greedy 1. 2. 7
Tests fortheMostPartofBinaryDecisionTables. . . . . . 29 1. 3
PartialDecision Rules. . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . .
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