比例优势boosting算法在高维有序多分类数据分析中的应用

The Application of Proportional Odds Boosting to the Analysis of Ordinal Multiclass High-dimensional Omics Data

目的探讨比例优势boosting算法在高维组学多分类有序数据中变量筛选和分类预测的应用。方法通过模拟实验和实例比较比例优势boosting算法和其他常用的多分类boosting算法在变量筛选和分类效果中的差异。结果模拟实验表明,比例优势boosting算法的变量筛选效果,尤其在小样本情况下要明显优于其他方式,分类效果略优于其他方式;实例数据分析结果表明,比例优势boosting变量筛选效果要优于其他方式,在分类效果上略低于随机梯度boosting,但优于其他boosting方式。结论比例优势boosting算法适用于高维有序多分类数据,具有实用价值。

Objective To explore the application of proportional odds boosting （P/O Boosting）model in variable selec- tion and classification in the case of ordinal multiclass high dimensional data. Methods We used simulated experiment and ac- tual data to compare the variable selection and classification of P/O Boosting and other common multiclass boosting algorithms. Results Simulation experiment suggested that P/O Boosting usually performs better than other methods, especially for small samples. Analysis results of actual data suggested that P/O Boosting outperforms than others in variable selection and except SG- BT in classification. Conclusion P/O Boosting is applicable to ordinal multiclass high-dimensional omics data and possessed oractical value.