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.
Complexity of Data Subsets Generated by the Random Subspace Method: an Experimental Investigation
ROLI, FABIO;
2001
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.
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