JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1014 10.6339/21-JDS1014 Statistical Data Science Random Machines: A Bagged-Weighted Support Vector Model with Free Kernel Choice AraAndersonalsouzara@gmail.com1∗ MaiaMateus1 LouzadaFrancisco2 MacêdoSamuel3 Department of Statistics, Federal University of Bahia, Salvador-BA, Brazil Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos-SP, Brazil Department of Natural Sciences and Mathemathics, Federal Inst. of Pernambuco, Recife-PE, Brazil Corresponding author. Email: alsouzara@gmail.com. 2021162021193409428 Supplementary Material

The proposed model called Random Machines (RM) was also implemented in R language and it can be used through the rmachines package, available and documented at GitHub https://github.com/MateusMaiaDS/rmachines. To a overall description of how to reproduce the results from this article just access the README at https://mateusmaiads.github.io/rmachines/.

91220202842021 2021 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2021 Open access article under the CC BY license.

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.

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