提出一种应用文本特征的类别属性进行文本分类过程中的类别噪声裁剪(Eliminating class noise,ECN)的算法.算法通过分析文本关键特征中蕴含的类别指示信息,主动预测待分类文本可能归属的类别集,从而减少参与决策的分类器数日,降低分类延...提出一种应用文本特征的类别属性进行文本分类过程中的类别噪声裁剪(Eliminating class noise,ECN)的算法.算法通过分析文本关键特征中蕴含的类别指示信息,主动预测待分类文本可能归属的类别集,从而减少参与决策的分类器数日,降低分类延迟,提高分类精度.在中、英文测试语料上的实验表明,该算法的F值分别达到0.76与0.93,而且分类器运行效率也有明显提升,整体性能较好.进一步的实验表明,此算法的扩展性能较好,结合一定的反馈学习策略,分类性能可进一步提高,其F值可达到0.806与0.943.展开更多
Differences are found in the attributes of microseismic events caused by coal seam rupture,underground structure activation,and groundwater movement in coal mine production.Based on these differences,accurate classific...Differences are found in the attributes of microseismic events caused by coal seam rupture,underground structure activation,and groundwater movement in coal mine production.Based on these differences,accurate classification and analysis of microseismic events are important for the water inrush warning of the coal mine working facefloor.Cluster analysis,which classifies samples according to data similarity,has remarkable advantages in nonlinear classification.A water inrush early warning method for coal minefloors is proposed in this paper.First,the short time average over long time average(STA/LTA)method is used to identify effective events from continuous microseismic records to realize the identification of microseismic events in coal mines.Then,ten attributes of microseismic events are extracted,and cluster analysis is conducted in the attribute domain to realize unsupervised classification of microseismic events.Clustering results of synthetic andfield data demonstrate the effectiveness of the proposed method.The analysis offield data clustering results shows that thefirst kind of events with time change rules is of considerable importance to the early warning of water inrush from the coal mine working facefloor.展开更多
文摘提出一种应用文本特征的类别属性进行文本分类过程中的类别噪声裁剪(Eliminating class noise,ECN)的算法.算法通过分析文本关键特征中蕴含的类别指示信息,主动预测待分类文本可能归属的类别集,从而减少参与决策的分类器数日,降低分类延迟,提高分类精度.在中、英文测试语料上的实验表明,该算法的F值分别达到0.76与0.93,而且分类器运行效率也有明显提升,整体性能较好.进一步的实验表明,此算法的扩展性能较好,结合一定的反馈学习策略,分类性能可进一步提高,其F值可达到0.806与0.943.
基金supported in part by the National Natural Science Foundation of China under Grant 41904098in part by the Beijing Nova Program under Grant 2022056in part by the National Natural Science Foundation of China (52174218)。
文摘Differences are found in the attributes of microseismic events caused by coal seam rupture,underground structure activation,and groundwater movement in coal mine production.Based on these differences,accurate classification and analysis of microseismic events are important for the water inrush warning of the coal mine working facefloor.Cluster analysis,which classifies samples according to data similarity,has remarkable advantages in nonlinear classification.A water inrush early warning method for coal minefloors is proposed in this paper.First,the short time average over long time average(STA/LTA)method is used to identify effective events from continuous microseismic records to realize the identification of microseismic events in coal mines.Then,ten attributes of microseismic events are extracted,and cluster analysis is conducted in the attribute domain to realize unsupervised classification of microseismic events.Clustering results of synthetic andfield data demonstrate the effectiveness of the proposed method.The analysis offield data clustering results shows that thefirst kind of events with time change rules is of considerable importance to the early warning of water inrush from the coal mine working facefloor.
