基于粗糙集的Logistic回归模型在矿井突水模式识别中的应用

矿井突水模式识别是一个非正态、非线性和高维数据处理问题,也是二分类问题。使用粗糙集属性约简算法对样本数据降维,建立Logistic回归模型,并利用粒子群算法对模型参数优化。该模型对建模样本突水模式识别正确率为90%,对测试样本突水模式识别正确率为100%,效果好于数据不降维的Logistic回归模型。该模型克服了线性回归分析解决二分类问题存在的不足,为矿井突水模式识别提供了一种新思路、新方法。

Application of Logistic regression model based on rough set in recognition of mine water inrush pattern

The mine water bursting pattern recognition is a non normal, nonlinear and high dimensional data processing problem, but also a binary-class problem. The attribute reduction algorithm of rough set was used to reduce the dimension of the sample data, to establish Logistic regression model, and particle swarm algorithm was used to optimize model parameters. The recognition accuracy of the model was 90% for water inrush mode of the modeling samples and 100% for water inrush mode of the testing samples, the effect was better than that of the Logistic regression model without dimensionality reduction. The model overcomes the shortcomings of the linear regression analysis for the solution of the binary-class problem, provides a new method for pattern recognition of mine water inrush.