Determination of Polynomial Degree in the Regression of Drug Combinations
Boqian Wang; Xianting Ding; Fei-Yue Wang
Source PublicationIEEE/CAA Journal of Automatica Sinica
2017
Volume4Issue:1Pages:41-47
Abstract 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.
KeywordCross-validation drug Combination Polynomial Regression Polynomial Degree Overfitting
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20315
Collection复杂系统管理与控制国家重点实验室_先进控制与自动化
Recommended Citation
GB/T 7714
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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