Original Contribution: Stacked generalization
Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Comparing Subspace Clusterings
IEEE Transactions on Knowledge and Data Engineering
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Evaluating clustering in subspace projections of high dimensional data
Proceedings of the VLDB Endowment
SubClass: classification of multidimensional noisy data using subspace clusters
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
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There can be multiple classifiers for a given data set. One way to generate multiple classifiers is to use subspaces of the attribute sets. In this paper, we generate subspace classifiers by an iterative convergence routine to build an ensemble classifier. Experimental evaluation covers the cases of both labelled and unlabelled (blind) data separately. We evaluate our approach on many benchmark UC Irvine datasets to assess the robustness of our approach with varying induced noise levels. We explicitly compare and present the utility of the clusterings generated for classification using several diverse clustering dissimilarity metrics. Results show that our ensemble classifier is a more robust classifier in comparison to different multi-class classification approaches.