Determination of Polynomial Degree in the Regression of Drug Combinations
Boqian Wang; Xianting Ding; Fei-Yue Wang
2017
发表期刊IEEE/CAA Journal of Automatica Sinica
卷号4期号:1页码:41-47
摘要 Studies on drug combinations are becoming more and more popular in the past few decades, with the development of computer and algorithms. One of the most common methods in optimizing drug combinations is regression of a polynomial model based on certain number of experimental observations. In this paper, we study how to determine the degree of polynomials in different circumstances of drug combination optimization. Using cross-validation, we have found that in most cases, a high degree results in failures of accurate prediction, named overfitting. An anti-noise test has also revealed that polynomial model with high degree tends to be less resistant to random errors in the observations.
关键词Cross-validation drug Combination Polynomial Regression Polynomial Degree Overfitting
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/20315
专题复杂系统管理与控制国家重点实验室_先进控制与自动化
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Boqian Wang,Xianting Ding,Fei-Yue Wang. Determination of Polynomial Degree in the Regression of Drug Combinations[J]. IEEE/CAA Journal of Automatica Sinica,2017,4(1):41-47.
APA Boqian Wang,Xianting Ding,&Fei-Yue Wang.(2017).Determination of Polynomial Degree in the Regression of Drug Combinations.IEEE/CAA Journal of Automatica Sinica,4(1),41-47.
MLA Boqian Wang,et al."Determination of Polynomial Degree in the Regression of Drug Combinations".IEEE/CAA Journal of Automatica Sinica 4.1(2017):41-47.
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