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In this paper, pruning techniques for the AdaBoost classifier are evaluated specially aimed for continuous learning in sensor mining applications. To assess the methods, three pruning schemes are evaluated using standard machine-learning benchmark datasets, simulated drifting datasets and real world cases. Early results obtained show that the pruning methodologies approach and sometimes out-perform the no-pruned version of the classifier, being at the same time more easily adaptable to the drift in the training distribution.
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| URL: |
http://sites.google.com/site/ajaybhushanmtech/research
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| Title: |
Pruning AdaBoost for Continuous Sensors Mining Applications |
| Image: |
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| Description: |
Early results obtained show that the pruning methodologies approach and sometimes out-perform the no-pruned version of the classifier, being at the same time more easily adaptable to the drift in the training distribution. |
| Category: |
Pruning Adaboost
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Continuous Sensors Mining Applications
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Standard Machine
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Learning Benchmark Datasets
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Simulated Drifting Datasets
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