Paper Info
Title
Complexity of Data Subsets Generated by the Random Subspace Method: An Experimental Investigation
Abstract
We report the results from an experimental investigation on the complexity of data subsets generated by the Random Subspace method. The main aim of this study is to analyse the variability of the complexity among the generated subsets. Four measures of complexity have been used, three from [4]: the minimal spanning tree (MST), the adherence subsets measure (ADH), the maximal feature efficiency (MFE); and a cluster label consistency measure (CLC) proposed in [7]. Our results with the UCI "wine" data set relate the variability in data complexity to the number of features used and the presence of redundant features.
Year
DOI
Venue
2001
10.1007/3-540-48219-9_35
Multiple Classifier Systems
Keywords
Field
DocType
minimal spanning tree
Computer science,Random subspace method,Algorithm,Information complexity,Data complexity,Minimum spanning tree,Statistical analysis
Conference
ISBN
Citations 
PageRank 
3-540-42284-6
5
0.53
References 
Authors
5
4
Name
Order
Citations
PageRank
Ludmila I. Kuncheva14942244.34
Fabio Roli24846311.69
Gian Luca Marcialis377460.54
Catherine A. Shipp425511.34