Abstract
Monitoring data in coal mine is essentially data stream, and missing coal mine monitoring data is caused by harsh coal mine environment, therefore coal mine safety evaluation can be seen as incomplete labeled data stream classification. The method is proposed for unlabeled data and concept drift in incomplete labeled data stream in this paper that uses semi-supervised learning method based on k-Modes algorithm and incremental decision tree model and concept drift detection mechanism based on clustering concept-cluster. Experimental results show the method can better label unlabeled data and detect concept drift in incomplete labeled data stream, and it has better classification accuracy for incomplete labeled data stream, and it provides a new practical approach for coal mine safety evaluation.
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