传统的基于机器学习的煤层突水预测方法需要大量的训练样本进行预测模型的训练。而在矿井突水问题中,大量训练样本的获得基本上是不可能的。主要研究在突水样本数据有限的情况下提高煤层突水预测结果的准确性。结合山西省某煤矿的实际情况,提出了一种新颖的基于万有引力的煤层底板突水预测方法(Gravitational force based algorithm,GFA)。该算法采用半监督的学习方式,将万有引力公式引入到预测模型中,利用少量的突水训练样本作为引力的源点吸引测试样本进行突水安全状态的传递,进而实现突水测试样本安全性的预测。将提出的算法用于历史突水数据以及实际的煤层底板突水数据进行实验,实验结果表明,在突水训练数据有限的情况下,提出的基于万有引力的煤层底板突水预测算法可获得良好的预测效果。
Ensemble learning is a wildly concerned issue.Traditional ensemble techniques are always adopted to seek better results with labeled data and base classifiers.They fail to address the ensemble task where only unlabeled data are available.A label propagation based ensemble(LPBE) approach is proposed to further combine base classification results with unlabeled data.First,a graph is constructed by taking unlabeled data as vertexes,and the weights in the graph are calculated by correntropy function.Average prediction results are gained from base classifiers,and then propagated under a regularization framework and adaptively enhanced over the graph.The proposed approach is further enriched when small labeled data are available.The proposed algorithms are evaluated on several UCI benchmark data sets.Results of simulations show that the proposed algorithms achieve satisfactory performance compared with existing ensemble methods.
