Paper Info
Title
Fast rule extraction with binary-coded relations
Abstract
The induction of rules is one of the key issues of the Rough Sets Theory (RST). Generally, this problem is equivalent to finding prime implicants of a Boolean function, which is an NP-hard combinatorial problem. In practice, the NP-hardness makes solving medium-sized and large real-life problems difficult. To counteract this we propose a new algorithm, in which representation of relations between objects are represented in the form of binary vectors. The relations considered are: indiscernibility and dominance. It is an important enhancement of the classic RST approach, in which only indiscernibility was taken into account. Evaluation of the proposed algorithm in experiments with numerous real-life data sets produced satisfactory results.
Year
Venue
Keywords
2003
Intell. Data Anal.
rough sets theory,boolean function,binary vector,np-hard combinatorial problem,binary-coded relation,important enhancement,classic rst approach,numerous real-life data,large real-life problem,new algorithm,fast rule extraction,proposed algorithm,knowledge discovery,decision rules
Field
DocType
Volume
Decision rule,Boolean function,Data set,Computer science,Algorithm,Rough set,Artificial intelligence,Knowledge extraction,Implicant,Dominance-based rough set approach,Machine learning,Binary number
Journal
7
Issue
Citations 
PageRank 
1
3
0.63
References 
Authors
3
2
Name
Order
Citations
PageRank
Roman Pindur1643.68
Robert Susmaga237033.32