摘要
人体腹部脂肪面积与人体腹部生物电阻抗和腹部皮下阻抗测量值存在着强非线性关系。采用人工蜂群算法对标记样本输入特征属性寻优获取最优解,通过支持向量回归机进行建模,计算赤池信息量准则(AIC)取得最优多项式模型。半监督学习方法通过反复预测大量未标记样本集,组建新的输入标记样本集进行重新寻优、训练、预测,计算最优AIC值的预测模型。通过测试样本集的标准差和相关性仿真实验表明,该模型具有较强的非线性函数逼近,能有效预测人体腹部脂肪面积。
There is a strong nonlinear relationship between body abdominal fat area and human abdominal bioelectrical impedance and abdominal subcutaneous impedance measurements. The artificial bee colony algorithm is used to get the optimal solution for the marking sample input feature attribute optimization. Through support vector machine regression modeling, we calculated the AIC(Akaike information criterion) to get the best polynomial model. Semi-supervised learning method repeatedly predicted a large number of unlabeled samples, and set up new input marker sets to reoptimize, train and predict, and calculated the best AIC value prediction model. By testing the standard deviation and correlation of the sample set, the simulation experiment shows that the model has strong nonlinear function approximation, and it can predict the abdominal fat area effectively.
作者
王建平
夏阳
李帷韬
李奇越
徐晓冰
Wang Jianping;Xia Yang;Li Weitao;Li Qiyue;Xu Xiaobing(School of Electrical Engineering and Automation,Hefei University of Technology,Heifei 230009,Anhui,China)
出处
《计算机应用与软件》
北大核心
2018年第9期264-269,287,共7页
Computer Applications and Software
基金
流程工业综合自动化国家重点实验室开放课题(PAL-N201605
PAL-N201504)
关键词
半监督学习
腹部脂肪面积
人工蜂群算法
支持向量回归机
赤池信息量准则
Senti-supervised learning
Abdominal fat area
Artificial bee colony
Support vector regression machine
Akanke information criterion