Probabilistic Dependencies in Linear Hierarchies of Decision Tables

Authors:
Wojciech Ziarko
Affiliations:
Department of Computer Science, University of Regina, Regina, Canada S4S 0A2
Venue:
Transactions on Rough Sets IX
Year:
2008

Citing 5
Cited 0

Variable precision rough set model

Journal of Computer and System Sciences
Rough Sets: Theoretical Aspects of Reasoning about Data

Rough Sets: Theoretical Aspects of Reasoning about Data
The investigation of the Bayesian rough set model

International Journal of Approximate Reasoning
Probabilistic rough sets

RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Rough membership and bayesian confirmation measures for parameterized rough sets

RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

The article is a study of probabilistic dependencies between attribute-defined partitions of a universe in hierarchies of probabilistic decision tables. The dependencies are expressed through two measures: the probabilistic generalization of the Pawlak's measure of the dependency between attributes and the expected certainty gain measure introduced by the author. The expected certainty gain measure reflects the subtle grades of probabilistic dependence of events. Both dependency measures are developed and it is shown how they can be extended from flat decision tables to dependencies existing in hierarchical structures of decision tables.