Democracy in neural nets: voting schemes for classification
Neural Networks
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Papyrus: a system for data mining over local and wide area clusters and super-clusters
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
The distributed boosting algorithm
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Model Combination in the Multiple-Data-Batches Scenario
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Identifying Relevant Databases for Multidatabase Mining
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Distributed Data Reduction through Agent Collaboration
KES-AMSTA '09 Proceedings of the Third KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
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Distributed data mining (DDM) is an important research area. The task of distributed data mining is to extract and integrate knowledge from different sources. Solving such tasks requires a special approach and tools, different from those applied to learning from data located in a single database. One of the approaches suitable for the DDM is to select relevant local patterns from the distributed databases. Such patterns often called prototypes, are subsequently merged to create a compact representation of the distributed data repositories. Next, the global classifier, called combiner, can be learned from such a compact representation. The paper proposes and reviews several strategies for constructing combiner classifiers to be used in solving the DDM tasks. Suggested strategies are evaluated experimentally. The evaluation process is based on several well-known benchmark data sets.