文章基本信息

标题：Random Machines: A Bagged-Weighted Support Vector Model with Free Kernel Choic
本地全文：下载
作者：Anderson Ara ; Mateus Maia ; Francisco Louzada 等
期刊名称：Journal of Data Science
印刷版ISSN：1680-743X
电子版ISSN：1683-8602
出版年度：2021
卷号：19
期号：3
页码：409-428
DOI：10.6339/21-JDS1014
语种：English
出版社：Tingmao Publish Company
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.