A systematic literature review on support vector machines applied to regression

Daniel Mavilo Calderon Nieto; Erik Alex Papa Quiroz; Miguel Angel Cano Lengua

doi:10.1109/SHIRCON53068.2021.9652268

A systematic literature review on support vector machines applied to regression

Daniel Mavilo Calderon Nieto, Erik Alex Papa Quiroz, Miguel Angel Cano Lengua

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

Abstract

This article aims to identify the current state of the art of the latest research related to models and algorithms in support vector machines for regression. For that, we use the methodology proposed by Kitchenham and Charter, in order to answer the following research questions: Q1: In which research areas is the support vector machine for regression most used? Q2. What optimization models are used to support vector machine for regression? Q3. What algorithms or optimization methods are used to solve support vector machine for regression? Q4. What nonconvex optimization models use support vector machine for regression? Q5. What optimization algorithms are used for nonconvex models to support vector machine for regression? We obtain valuable information about the questions to construct new models and algorithms in this research area.

Original language	English
Title of host publication	Proceedings of the 2021 IEEE Sciences and Humanities International Research Conference, SHIRCON 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665429146
DOIs	https://doi.org/10.1109/SHIRCON53068.2021.9652268
State	Published - 2021
Event	5th IEEE Sciences and Humanities International Research Conference, SHIRCON 2021 - Lima, Peru Duration: 17 Nov 2021 → 19 Nov 2021

Publication series

Name	Proceedings of the 2021 IEEE Sciences and Humanities International Research Conference, SHIRCON 2021

Conference

Conference	5th IEEE Sciences and Humanities International Research Conference, SHIRCON 2021
Country/Territory	Peru
City	Lima
Period	17/11/21 → 19/11/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

Support vector regression (SVR)
non-convex optimization models
optimization algorithms

Access to Document

10.1109/SHIRCON53068.2021.9652268

Cite this

Nieto, D. M. C., Quiroz, E. A. P., & Cano Lengua, M. A. (2021). A systematic literature review on support vector machines applied to regression. In Proceedings of the 2021 IEEE Sciences and Humanities International Research Conference, SHIRCON 2021 (Proceedings of the 2021 IEEE Sciences and Humanities International Research Conference, SHIRCON 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SHIRCON53068.2021.9652268

Nieto, Daniel Mavilo Calderon ; Quiroz, Erik Alex Papa ; Cano Lengua, Miguel Angel. / A systematic literature review on support vector machines applied to regression. Proceedings of the 2021 IEEE Sciences and Humanities International Research Conference, SHIRCON 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings of the 2021 IEEE Sciences and Humanities International Research Conference, SHIRCON 2021).

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title = "A systematic literature review on support vector machines applied to regression",

abstract = "This article aims to identify the current state of the art of the latest research related to models and algorithms in support vector machines for regression. For that, we use the methodology proposed by Kitchenham and Charter, in order to answer the following research questions: Q1: In which research areas is the support vector machine for regression most used? Q2. What optimization models are used to support vector machine for regression? Q3. What algorithms or optimization methods are used to solve support vector machine for regression? Q4. What nonconvex optimization models use support vector machine for regression? Q5. What optimization algorithms are used for nonconvex models to support vector machine for regression? We obtain valuable information about the questions to construct new models and algorithms in this research area.",

keywords = "Support vector regression (SVR), non-convex optimization models, optimization algorithms",

author = "Nieto, {Daniel Mavilo Calderon} and Quiroz, {Erik Alex Papa} and {Cano Lengua}, {Miguel Angel}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 5th IEEE Sciences and Humanities International Research Conference, SHIRCON 2021 ; Conference date: 17-11-2021 Through 19-11-2021",

year = "2021",

doi = "10.1109/SHIRCON53068.2021.9652268",

language = "Ingl{\'e}s",

series = "Proceedings of the 2021 IEEE Sciences and Humanities International Research Conference, SHIRCON 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "Proceedings of the 2021 IEEE Sciences and Humanities International Research Conference, SHIRCON 2021",

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Nieto, DMC, Quiroz, EAP & Cano Lengua, MA 2021, A systematic literature review on support vector machines applied to regression. in Proceedings of the 2021 IEEE Sciences and Humanities International Research Conference, SHIRCON 2021. Proceedings of the 2021 IEEE Sciences and Humanities International Research Conference, SHIRCON 2021, Institute of Electrical and Electronics Engineers Inc., 5th IEEE Sciences and Humanities International Research Conference, SHIRCON 2021, Lima, Peru, 17/11/21. https://doi.org/10.1109/SHIRCON53068.2021.9652268

A systematic literature review on support vector machines applied to regression. / Nieto, Daniel Mavilo Calderon; Quiroz, Erik Alex Papa; Cano Lengua, Miguel Angel.
Proceedings of the 2021 IEEE Sciences and Humanities International Research Conference, SHIRCON 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings of the 2021 IEEE Sciences and Humanities International Research Conference, SHIRCON 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - A systematic literature review on support vector machines applied to regression

AU - Nieto, Daniel Mavilo Calderon

AU - Quiroz, Erik Alex Papa

AU - Cano Lengua, Miguel Angel

PY - 2021

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N2 - This article aims to identify the current state of the art of the latest research related to models and algorithms in support vector machines for regression. For that, we use the methodology proposed by Kitchenham and Charter, in order to answer the following research questions: Q1: In which research areas is the support vector machine for regression most used? Q2. What optimization models are used to support vector machine for regression? Q3. What algorithms or optimization methods are used to solve support vector machine for regression? Q4. What nonconvex optimization models use support vector machine for regression? Q5. What optimization algorithms are used for nonconvex models to support vector machine for regression? We obtain valuable information about the questions to construct new models and algorithms in this research area.

AB - This article aims to identify the current state of the art of the latest research related to models and algorithms in support vector machines for regression. For that, we use the methodology proposed by Kitchenham and Charter, in order to answer the following research questions: Q1: In which research areas is the support vector machine for regression most used? Q2. What optimization models are used to support vector machine for regression? Q3. What algorithms or optimization methods are used to solve support vector machine for regression? Q4. What nonconvex optimization models use support vector machine for regression? Q5. What optimization algorithms are used for nonconvex models to support vector machine for regression? We obtain valuable information about the questions to construct new models and algorithms in this research area.

KW - Support vector regression (SVR)

KW - non-convex optimization models

KW - optimization algorithms

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DO - 10.1109/SHIRCON53068.2021.9652268

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AN - SCOPUS:85131708728

T3 - Proceedings of the 2021 IEEE Sciences and Humanities International Research Conference, SHIRCON 2021

BT - Proceedings of the 2021 IEEE Sciences and Humanities International Research Conference, SHIRCON 2021

PB - Institute of Electrical and Electronics Engineers Inc.

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Nieto DMC, Quiroz EAP, Cano Lengua MA. A systematic literature review on support vector machines applied to regression. In Proceedings of the 2021 IEEE Sciences and Humanities International Research Conference, SHIRCON 2021. Institute of Electrical and Electronics Engineers Inc. 2021. (Proceedings of the 2021 IEEE Sciences and Humanities International Research Conference, SHIRCON 2021). doi: 10.1109/SHIRCON53068.2021.9652268

A systematic literature review on support vector machines applied to regression

Abstract

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Bibliographical note

Keywords

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