Paper Info
Title
Probabilistic Rough Induction: The GDT-RS Methodology and Algorithms
Abstract
In this paper, we introduce a probabilistic rough induction methodology and discuss two algorithms for its implementation. This methodology is based on the combination of Generalization Distribution Table (GDT) and the Rough Set theory (GDT-RS for short). A GDT is a table in which the probabilistic relationships between concepts and instances over discrete domains are represented. The GDT provides a probabilistic basis for evaluating the strength of a rule. The rough set theory is used to find minimal relative reducts from the set of rules with larger strength. Main features of the GDT-RS are (1) biases can be selected flexibly for search control, and background knowledge can be used as a bias to control the creation of a GDT and the rule induction process; (2) the uncertainty of a rule including the prediction of possible instances can be represented explicitly in the strength of the rule.
Year
DOI
Venue
1999
10.1007/BFb0095151
ISMIS
Keywords
Field
DocType
probabilistic rough induction,gdt-rs methodology,rough set theory
Decision table,Computer science,Decision support system,Algorithm,Rough set,Rule induction,Artificial intelligence,Probabilistic logic,Machine learning
Conference
ISBN
Citations 
PageRank 
3-540-65965-X
12
1.67
References 
Authors
5
3
Name
Order
Citations
PageRank
Juzhen Dong121417.05
Ning Zhong22907300.63
Setsuo Ohsuga3960222.02