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%.

machine learning, multilabel classification, air pollution.