Abstract
Choose data Mining to study the anomaly detection in coal preparation, using ash of raw coal , rapid ash and yields of raw coal which density below 1.45, and ash and actual yields of fine coal in the database as sample attribute of coal production anomaly detection model, based on Box-plot analysis, the evaluating values range of five attribute above are determined. On this condition, by using SVM and KNN, the identification model of anomaly detection in coal preparation is established. The Receiver Operating Characteristic curves analysis result shows judging production target Abnormal Conditions using SVM will be more accurate in coal preparation.
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