计算机科学与探索2019，Vol.13Issue(3)：457-467,11.
方差正则化的分类模型选择准则*
Variance-Regularized Classification Model Selection Criterion*
房立超 1王钰 2杨杏丽 3李济洪1
作者信息
- 1. 山西大学 数学科学学院,太原 030006
- 2. 山西大学 现代教育技术学院,太原 030006
- 3. 山西大学 软件学院,太原 030006
- 折叠
摘要
Abstract
In traditional machine learning, model selection is always directly performed based on the estimation of one performance measure index, without considering the variance of the estimation. However, this neglection may probably lead to the selection of a wrong model. Therefore, a method of adding the information of variance into the study of classification model selection is considered in order to improve the generalization ability of the selected model, that is, the variance estimation of the block 3×2 cross-validation estimation of the generalization error is added as a regularization term into the traditional model selection criterion, and a new variance-regularized classification model selection criterion is proposed. The simulated and real data experiments show that the proposed model selection criterion has a higher probability to select the correct classification model in the classification model selection problem compared to the traditional methods. The importance of variance in model selection and the effectiveness of the proposed model selection criteria are also validated. Furthermore, the consistency in selection of the proposed criterions is theoretically proven in the model selection task of two-class classification problem.关键词
模型选择/泛化误差/组块3×2交叉验证/方差正则化Key words
model selection/ generalization error/ blocked 3×2 cross-validation/ variance-regularized分类
信息技术与安全科学引用本文复制引用
房立超,王钰,杨杏丽,李济洪..方差正则化的分类模型选择准则*[J].计算机科学与探索,2019,13(3):457-467,11.
