Paper Info
Title
Weighting of Attributes in an Embedded Rough Approach.
Abstract
In an embedded approach to feature selection and reduction, a mechanism determining their choice constitutes a part of an inductive learning algorithm, as happens for example in construction of decision trees, artificial neural networks with pruning, or rough sets with activated relative reducts. The paper presents the embedded solution based on assumed weights for reducts and measures defined for conditional attributes, where weighting of these attributes was used in their backward elimination for rule classifiers induced in Dominance-Based Rough Set Approach. The methodology is illustrated with a binary classification case of authorship attribution.
Year
DOI
Venue
2013
10.1007/978-3-319-02309-0_52
MAN-MACHINE INTERACTIONS 3
Keywords
Field
DocType
feature selection,reduction,embedded approach,DRSA,reducts,weighting,authorship attribution,stylometry
Decision tree,Weighting,Pattern recognition,Feature selection,Binary classification,Computer science,Rough set,Stylometry,Artificial intelligence,Artificial neural network,Pruning
Conference
Volume
ISSN
Citations 
242
1867-5662
0
PageRank 
References 
Authors
0.34
6
1
Name
Order
Citations
PageRank
Urszula Stanczyk1193.75