International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Machine Learning
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Knowledge grid support for treatment of traumatic brain injury victims
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartI
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Classification deals with discovery of a predictive learning function that classifies a data object into one of several predefined classes. We present a novel decision-tree-based classification service which can be used either autonomously or as a building block to construct distributed and scalable classifiers that operate on data repositories integrated into the Grid that typically involve large, complex, heterogeneous, and geographically distributed datasets. Although classification is considered as a well-studied problem – a lot of classification methods were proposed for sequential, parallel and distributed environments, so far, to our best knowledge, no effort was devoted to building classifiers based on federation of Grid resources. The Grid service described in this paper was fully implemented and integrated into the GridMiner framework (www.gridminer.org). Scalability and performance of the prototype implementation have been examined and the results are presented.