基于交替方向乘子法的广义交互LASSO模型用于肝脏疾病分类

Generalized interaction LASSO based on alternating direction method of multipliers for liver disease classification

肝脏疾病特征及交互特征对于肝脏疾病的分类具有重要意义,本文在交互最小绝对收缩和选择算子(LASSO)模型的基础上,研究了广义交互LASSO模型并与其他可用于肝脏疾病分类的方法比较。首先,本文建立了广义交互逻辑斯特(logistic)分类模型,在模型参数中添加LASSO罚函数,然后将模型参数通过交替方向乘子法(ADMM)求解,得到模型系数的稀疏解。最后将测试样本代入模型,按照最大概率进行分类结果统计。通过将本文方法应用在肝脏失调数据集和印度肝病数据集的数据实验结果表明,交互特征的模型系数不为零,这说明交互特征对分类存在贡献。最终结果表明,本文提出的广义交互LASSO方法的正确率要优于交互LASSO方法,也优于传统模式识别方法,可将广义交互LASSO方法推广应用到其他疾病的分类问题上。

Features and interaction between features of liver disease is of great significance for the classification of liver disease. Based on least absolute shrinkage and selection operator （LASSO） and interaction LASSO, the generalized interaction LASSO model is proposed in this paper for liver disease classification and compared with other methods. Firstly, the generalized interaction logistic classification model was constructed and the LASSO penalty constraints were added to the interactive model parameters. Then the model parameters were solved by an efficient alternating directions method of multipliers （ADMM） algorithm. The solutions of model parameters were sparse. Finally, the test samples were fed to the model and the classification results were obtained by the largest statistical probability. The experimental results of liver disorder dataset and India liver dataset obtained by the proposed methods showed that the coefficients of interaction features of the model were not zero, indicating that interaction features were contributive to classification. The accuracy of the generalized interaction LASSO method is better than that of the interaction LASSO method, and it is also better than that of traditional pattern recognition methods. The generalized interaction LASSO method can also be popularized to other disease classification areas.