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- W3210402114 abstract "In high-dimensional data, penalized regression is often used for variable selection and parameter estimation. However, these methods typically require time-consuming cross-validation methods to select tuning parameters and retain more false positives under high dimensionality. This chapter discusses sparse boosting based machine learning methods in the following high-dimensional problems. First, a sparse boosting method to select important biomarkers is studied for the right censored survival data with high-dimensional biomarkers. Then, a two-step sparse boosting method to carry out the variable selection and the model-based prediction is studied for the high-dimensional longitudinal observations measured repeatedly over time. Finally, a multi-step sparse boosting method to identify patient subgroups that exhibit different treatment effects is studied for the high-dimensional dense longitudinal observations. This chapter intends to solve the problem of how to improve the accuracy and calculation speed of variable selection and parameter estimation in high-dimensional data. It aims to expand the application scope of sparse boosting and develop new methods of high-dimensional survival analysis, longitudinal data analysis, and subgroup analysis, which has great application prospects." @default.
- W3210402114 created "2021-11-08" @default.
- W3210402114 creator A5040689829 @default.
- W3210402114 date "2021-10-19" @default.
- W3210402114 modified "2023-10-15" @default.
- W3210402114 title "Sparse Boosting Based Machine Learning Methods for High-Dimensional Data" @default.
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- W3210402114 doi "https://doi.org/10.5772/intechopen.100506" @default.
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