文章基本信息

标题：Nature inspired optimization tools for SVMs - NIOTS
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作者：Carlos Eduardo da Silva Santos ; Leandro dos Santos Coelho ; Carlos Humberto Llanos 等
期刊名称：MethodsX
印刷版ISSN：2215-0161
电子版ISSN：2215-0161
出版年度：2021
卷号：8
页码：1-9
DOI：10.1016/j.mex.2021.101574
语种：English
出版社：Elsevier
摘要：AbstractSupport Vector Machines (SVMs) technique for achieving classifiers and regressors. However, to obtain models with high accuracy and low complexity, it is necessary to define the kernel parameters as well as the parameters of the training model, which are called hyperparameters. The challenge of defining the more suitable value to hyperparameters is called the Parameter Selection Problem (PSP). However, minimizing the complexity and maximizing the generalization capacity of the SVMs are conflicting criteria. Therefore, we propose the Nature Inspired Optimization Tools for SVMs (NIOTS) that offers a method to automate the search process for the best possible solution for the PSP, allowing the user to quickly obtain several sets of good solutions and choose the one most appropriate for his specific problem.•The PSP has been modeled as a Multiobjective Optimization Problem (MOP) with two objectives: (1) good precision and (2) low complexity (low number of support vectors).•The user can evaluate multiple solutions included in the Pareto front, in terms of precision and low complexity of the model.•Apart from the Adaptive Parameter with Mutant Tournament Multiobjective Differential Evolution (APMT-MODE), the user can choose other metaheuristics and also among several kernel options.Graphical AbstractDisplay Omitted
关键词：Support vectors machines;Parameters selection problem;Adaptive parameters control;Differential evolution algorithm;Multi-objective optimization problem