基于粗集理论的决策树在信用卡发放中的应用

基于粗集和决策树两种方法的各自优势互补,提出将粗集与决策树相结合的新方法,并将此算法运用到个人信用卡发放模型中。首先利用布尔推理算法将连续属性进行离散化处理,然后采用一种以加权和属性重要度为启发信息进行属性约简,得到降维数据,最后采用J48决策树算法,得到决策规则。通过对比K最近邻分类、朴素贝叶斯、RBF神经网络、支持向量机等算法,这种新的数据挖掘算法保留了原有数据特点,加快了知识获取的进程,提高了模型的交叉验证率,简化了规则,取得了满意的研究结果。

主成分-距离水质识别水源模型主成分的选取

为了寻找最佳主成分以使水质识别水源模型效果最好,以焦作矿区水质数据为例建立主成分-距离水源识别模型并进行回代验证和交叉验证,正确识别率达到82.9%;同时对四个不同矿区分别建立模型,发现主成分分别选取4、4、4、3个时识别效果最好,回代正确率和交叉正确率分别达到87.5%、97.1%、93.9%、93.1%和83.3%、94.3%、90.9%,89.7%。结果表明:主成分-距离水质识别水源模型能够很好地判别煤矿突水来源;在交叉验证率最大时,选取最大回代验证率对应的主成分,模型识别效果最好。

A Bregman adaptive sparse-spike deconvolution method in the frequency domain

To improve the anti-noise performance of the time-domain Bregman iterative algorithm,an adaptive frequency-domain Bregman sparse-spike deconvolution algorithm is proposed.By solving the Bregman algorithm in the frequency domain,the influence of Gaussian as well as outlier noise on the convergence of the algorithm is effectively avoided.In other words,the proposed algorithm avoids data noise effects by implementing the calculations in the frequency domain.Moreover,the computational efficiency is greatly improved compared with the conventional method.Generalized cross validation is introduced in the solving process to optimize the regularization parameter and thus the algorithm is equipped with strong self-adaptation.Different theoretical models are built and solved using the algorithms in both time and frequency domains.Finally,the proposed and the conventional methods are both used to process actual seismic data.The comparison of the results confirms the superiority of the proposed algorithm due to its noise resistance and self-adaptation capability.