Conceptual Modeling of Coincident Failures in Multiversion Software
IEEE Transactions on Software Engineering
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Complexity of Classification Problems and Comparative Advantages of Combined Classifiers
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Combinations of weak classifiers
IEEE Transactions on Neural Networks
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Bias-Variance Analysis and Ensembles of SVM
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
The use of artificial-intelligence-based ensembles for intrusion detection: a review
Applied Computational Intelligence and Soft Computing
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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.