Random Machines: A Bagged-Weighted Support Vector Model with Free Kernel Choice

Journal of Data Science

article

Anderson Ara¹  Mateus Maia¹  Francisco Louzada²  Samuel Macêdo³ 

[1] Department of Statistics, Federal University of Bahia;Institute of Mathematical and Computer Sciences, University of São Paulo;Department of Natural Sciences and Mathemathics

关键词: bagging;    kernel functions;    support vector machines;   

DOI  :  10.6339/21-JDS1014

学科分类：土木及结构工程学

来源: JDS

【 摘 要 】

Improvement of statistical learning models to increase efficiency in solving classification or regression problems is a goal pursued by the scientific community. Particularly, the support vector machine model has become one of the most successful algorithms for this task. Despite the strong predictive capacity from the support vector approach, its performance relies on the selection of hyperparameters of the model, such as the kernel function that will be used. The traditional procedures to decide which kernel function will be used are computationally expensive, in general, becoming infeasible for certain datasets. In this paper, we proposed a novel framework to deal with the kernel function selection called Random Machines. The results improved accuracy and reduced computational time, evaluated over simulation scenarios, and real-data benchmarking.

【 授权许可】

CC BY