Parameter Optimization of Support Vector Regression Using Harris Hawks Optimization

被引：22

作者：

Setiawan, I. Nyoman ^{[1
]}

Kurniawan, Robert ^{[1
]}

Yuniarto, Budi ^{[1
]}

Caraka, Rezzy Eko ^{[2
,3
]}

Pardamean, Bens ^{[3
,4
]}

机构：

[1] Polytech Stat STIS, Computat Stat Dept, Jakarta 13330, Indonesia

[2] Chaoyang Univ Technol, Coll Informat, Dept Informat Management, Taichung, Taiwan

[3] Bina Nusantara Univ, Bioinformat & Data Sci Res Ctr, Jakarta 11530, Indonesia

[4] Bina Nusantara Univ, Comp Sci Dept, BINUS Grad Program, Comp Sci Program, Jakarta 11530, Indonesia

来源：

5TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND COMPUTATIONAL INTELLIGENCE 2020 | 2021年 / 179卷

关键词：

SVR; harris hawks optimization; parameter optimization; kernel; forecasting; MACHINES;

D O I：

10.1016/j.procs.2021.12.003

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Support Vector Regression (SVR) is often used in forecasting. Adjustment of parameters in the SVR affects the results of forecasting. This study aims to analyze the SVR method that is optimized using Harris Hawks Optimization (HHO), hereinafter referred to as HHO-SVR. The HHO-SVR was evaluated using five benchmark datasets to determine the performance of this method. The HHO process is also compared based on the type of kernel and other metaheuristic algorithms. The results showed that the HHO-SVR has almost the same performance as other methods but is less efficient in terms of time. In addition, the type of kernel also affects the process and results. (C) 2021 The Authors. Published by Elsevier B.V.

引用

页码：17 / 24

页数：8

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