Advances in Computer Science and Engineering
Volume 13, Issue 1, Pages 69 - 72
(August 2014)
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ENSEMBLES OF BINARY DECISION TREES FOR PREDICTING AIR QUALITY
H. F. Jelinek, A. V. Kelarev, A. Kolbe, S. Heidenreich and T. Oakman
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Abstract:
This paper concentrates on applying a novel data mining algorithm, the Ensemble of Binary Decision Trees, EBDT, for the detection and monitoring of environmental particulate matter in high-risk areas due to agricultural stubble burning. Experimental outcomes presented here show that the EBDT classifier based on J48 achieved the best outcome for the detection of PM2.5 patterns with an accuracy of 83.70%. |
| Keywords and phrases: machine learning, multilabel classification, air pollution. |
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