Maximizing the predictive value of production rules
Artificial Intelligence
Proceedings of the sixth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Generating production rules from decision trees
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
Generating C4.5 production rules in parallel
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Scaling up: distributed machine learning with cooperation
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Distributed Pasting of Small Votes
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
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In this paper a concern about the accuracy (as a function of parallelism) of a certain class of distributed learning algorithms is raised, and one proposed improvement is illustrated.We focus on learning a single model from a set of disjoint data sets, which are distributed across a set of computers. The model is a set of rules. The distributed data sets may be disjoint for any of several reasons. In our approach, the first step is to construct a rule set (model) for each of the original disjoint data sets. Then rule sets are merged until an eventual final rule set is obtained which models the aggregate data. We show that this approach compares to directly creating a rule set from the aggregate data and promises faster learning. Accuracy can drop off as the degree of parallelism increases. However, an approach has been developed to extend the degree of parallelism achieved before this problem takes over.