基于混合采样和Stacking集成的电信用户网别预测

为了准确识别潜在换网电信用户,建立一种电信用户网别更换预测模型。根据用户历史数据生成网别更换标签,确定其多数类和少数类样本。利用具有噪声的密度聚类欠采样方法对多数类样本进行聚类,删除聚类后的噪声样本和各簇的边界样本,并选择各簇核心样本点进行随机欠采样;结合人工合成少数类过采样方法对少数类样本进行过采样。将构成混合采样后的平衡样本集合,输入到两层的Stacking集成学习算法中训练,得出分类结果。实验结果表明,该模型具有较好的数据集均衡性能,且预测准确率高,能够更好地识别潜在的网别更换用户。

Pattern recognition and prediction study of rock burst based on neural network

Many monitoring measures were used in the production field for predicting rockburst.However, predicting rock burst according to complicated observation data is alwaysa pressing problem in this research field.Though the critical value method gets extensiveapplication in practice, it stresses only on the superficial change of data and overlooks alot of features of rock burst and useful information that is concealed and hidden in the observationtime series.Pattern recognition extracts the feature value of time domain, frequencydomain and wavelet domain in observation time series to form Multi-Feature vectors,using Euclidean distance measure as the separable criterion between the same typeand different type to compress and transform feature vectors.It applies neural network asa tool to recognize the danger of rock burst, and uses feature vectors being compressedto carry out training and studying.It is proved by test samples that predicting precisionshould be prior to such traditional predicting methods as pattern recognition and critical indicatormethod.