特征交互lasso用于肝病分类
发布时间：2019-03-22 14:56
【摘要】：针对肝病分类中存在的特征交互的问题,我们研究了一种分层交互lasso分类方法。首先对logistic模型添加lasso罚函数和分层凸约束,其次采用卡罗需-库恩-塔克条件与广义梯度下降法相结合的凸优化方法给出模型求解方法,最后得到主效应特征系数与交互特征系数的稀疏解,实现模型分类。本文在两个肝病数据集上进行实验,证明了特征交互对肝病分类有贡献。实验结果证明了分层交互lasso方法可解释性强,效果、效率均优于lasso方法、全特征对lasso方法以及支持向量机、最近邻和决策树等传统分类方法。
[Abstract]:In order to solve the problem of the character interaction in the classification of liver disease, we have studied a hierarchical interactive lasso classification method. In this paper, the lasso penalty function and the hierarchical convex constraint are added to the logistic model, and then the method of the model is given by the convex optimization method which is combined with the generalized gradient descent method by the Caro-Kuhn-Tucker condition and the generalized gradient descent method, and finally, the sparse solution of the main effect characteristic coefficient and the interaction characteristic coefficient is obtained, and the model classification is realized. In this paper, we experiment on the data set of two liver diseases, and it is proved that the characteristic interaction contributes to the classification of liver diseases. The experimental results show that the layered interactive lasso method can be interpreted as strong, the effect and the efficiency are better than that of the lasso method, the whole characteristic is the lasso method, the support vector machine, the nearest neighbor and the decision tree, and the like.
【作者单位】： 燕山大学信息科学与工程学院;
【基金】：国家自然科学基金资助项目(61473339) 中国博士后科学基金资助项目(2014M561202) 河北省2014年度博士后专项资助项目(B2014010005) 首批“河北省青年拔尖人才”资助项目
【分类号】：R575
，
本文编号：2445689
[Abstract]:In order to solve the problem of the character interaction in the classification of liver disease, we have studied a hierarchical interactive lasso classification method. In this paper, the lasso penalty function and the hierarchical convex constraint are added to the logistic model, and then the method of the model is given by the convex optimization method which is combined with the generalized gradient descent method by the Caro-Kuhn-Tucker condition and the generalized gradient descent method, and finally, the sparse solution of the main effect characteristic coefficient and the interaction characteristic coefficient is obtained, and the model classification is realized. In this paper, we experiment on the data set of two liver diseases, and it is proved that the characteristic interaction contributes to the classification of liver diseases. The experimental results show that the layered interactive lasso method can be interpreted as strong, the effect and the efficiency are better than that of the lasso method, the whole characteristic is the lasso method, the support vector machine, the nearest neighbor and the decision tree, and the like.
【作者单位】： 燕山大学信息科学与工程学院;
【基金】：国家自然科学基金资助项目(61473339) 中国博士后科学基金资助项目(2014M561202) 河北省2014年度博士后专项资助项目(B2014010005) 首批“河北省青年拔尖人才”资助项目
【分类号】：R575
，
本文编号：2445689
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