Cluster-Based cumulative ensembles

Authors:
Hanan G. Ayad;Mohamed S. Kamel
Affiliations:
Pattern Analysis and Machine Intelligence Lab, Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada;Pattern Analysis and Machine Intelligence Lab, Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada
Venue:
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Year:
2005

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Abstract

In this paper, we propose a cluster-based cumulative representation for cluster ensembles. Cluster labels are mapped to incrementally accumulated clusters, and a matching criterion based on maximum similarity is used. The ensemble method is investigated with bootstrap re-sampling, where the k-means algorithm is used to generate high granularity clusterings. For combining, group average hierarchical meta-clustering is applied and the Jaccard measure is used for cluster similarity computation. Patterns are assigned to combined meta-clusters based on estimated cluster assignment probabilities. The cluster-based cumulative ensembles are more compact than co-association-based ensembles. Experimental results on artificial and real data show reduction of the error rate across varying ensemble parameters and cluster structures.