This paper proposed a coupled model of effective feature extraction and optimized classifier, which can overcome the existing problems of coal and gas outbursts classification in the literatures. Firstly, we support the use of kernel principal component analysis and linear discriminant analysis methods to extract linear and nonlinear feature information from coal and gas outbursts influencing factors. Secondly, in order to realize the complementarity and correlation between coal and gas outbursts influencing factors, we perform parallel feature fusion to combine the extracted linear and nonlinear feature information. Thirdly, an improved classifier called BO–MKRVM has been proposed that combines mixed kernel relevance vector machine (MKRVM) and Bayesian optimization (BO) algorithm to predict coal and gas outbursts according to the extracted features. In BO–MKRVM model, an effective mixed kernel function which combines Gaussian kernel function and Sigmoid kernel function is proposed to improve the learning and generalization ability of the MKRVM classifier, then the BO and tenfold cross-validation are utilized to optimize kernel parameters and weight coefficients of MKRVM with strong global and local search capability, the proposed BO–MKRVM classifier is performed on coal and gas outbursts dataset. Compared with the single feature extraction method, the combination of linear and non-linear feature extraction methods can obtain complete feature information and contribute to the classification performance of outbursts. The mixed kernel function considers the characteristics of coal and gas outbursts sample data, which can also effectively improve the outbursts accuracy. After the MKRVM classifier is optimized by BO algorithm, the BO–MKRVM classifier has better fitting effect and generalization ability, and obtains higher accuracy with a lower time. The experimental results are obviously better than those of other classification models, which further verifies the applicability of the proposed coupled model.

提出了有效特征提取与优化分类器的耦合模型，可以克服文献中存在的煤与瓦斯突出分类问题。首先，我们支持使用核主成分分析和线性判别分析方法从煤和瓦斯突出影响因素中提取线性和非线性特征信息。其次，为了实现煤与瓦斯突出影响因素之间的互补性和相关性，我们进行并行特征融合，将提取的线性和非线性特征信息进行组合。第三，提出了一种改进的分类器BO–MKRVM，该分类器结合了混合核相关向量机（MKRVM）和贝叶斯优化（BO）算法，可根据提取的特征预测煤与瓦斯突出。在BO–MKRVM模型中，提出了一种有效的混合核函数，将高斯核函数和Sigmoid核函数相结合，以提高MKRVM分类器的学习和泛化能力，然后利用BO和十倍交叉验证来优化核参数和权重具有强大的全局和局部搜索能力的MKRVM系数，提出的BO–MKRVM分类器是在煤和瓦斯突出数据集上进行的。与单特征提取方法相比，线性和非线性特征提取方法的结合可以获得完整的特征信息，并有助于突出的分类性能。混合核函数考虑了煤与瓦斯突出样本数据的特征，也可以有效地提高突出精度。通过BO算法对MKRVM分类器进行优化后，BO–MKRVM分类器具有更好的拟合效果和泛化能力，并且可以在更短的时间内获得更高的准确性。实验结果明显优于其他分类模型，这进一步验证了所提出的耦合模型的适用性。这也可以有效提高突出的准确性。通过BO算法对MKRVM分类器进行优化后，BO–MKRVM分类器具有更好的拟合效果和泛化能力，并且可以在更短的时间内获得更高的准确性。实验结果明显优于其他分类模型，这进一步验证了所提出的耦合模型的适用性。这也可以有效提高突出的准确性。通过BO算法对MKRVM分类器进行优化后，BO–MKRVM分类器具有更好的拟合效果和泛化能力，并且可以在更短的时间内获得更高的准确性。实验结果明显优于其他分类模型，这进一步验证了所提出的耦合模型的适用性。