research-article

An Intelligent Model Validation Method Based on ECOC SVM

Authors:

Ping MaAuthors Info & Claims

ICCMS '18: Proceedings of the 10th International Conference on Computer Modeling and Simulation

Pages 67 - 71

https://doi.org/10.1145/3177457.3177487

Published: 08 January 2018 Publication History

Abstract

This paper develops an intelligent model validation method based on error correcting output coding support vector machine (ECOC SVM). The similarity analysis between simulation time series from computerized model and observed time series from real-world system is formulated as a multi-class classification problem. The ECOC framework, built on the basis of the error correcting principles of communication theory, decomposes the multi-class classification task as multiple binary classification problems. The SVM is used as the base classifier and a set of similarity measure methods is applied to extract the input features. Compared to conventional methods, the proposed validation method based on ECOC SVM incorporates multiple similarity measures to a comprehensive similarity measure and can learn to predict the credibility level from training samples. The application result reveals that the classification accuracy achieved 82%, which means the proposed method is promising for the similarity analysis of large datasets.

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Cited By

Elsayad AMubarak FAbdullah HFahhad MSaad N(2024)Advancements In Passive Landmine Detection A Multiclass Approach With Fluxgate Sensor And Machine Learning Models2024 41st National Radio Science Conference (NRSC)10.1109/NRSC61581.2024.10510535(158-165)Online publication date: 16-Apr-2024
https://doi.org/10.1109/NRSC61581.2024.10510535
Han XPeng J(2023)Bird sound classification based on ECOC-SVMApplied Acoustics10.1016/j.apacoust.2023.109245204(109245)Online publication date: Mar-2023
https://doi.org/10.1016/j.apacoust.2023.109245
Jain KGupta MPatel SAbraham A(2023)Object Classification Using ECOC Multi-class SVM and HOG CharacteristicsIntelligent Systems Design and Applications10.1007/978-3-031-27440-4_3(23-33)Online publication date: 31-May-2023
https://doi.org/10.1007/978-3-031-27440-4_3
Show More Cited By

Index Terms

An Intelligent Model Validation Method Based on ECOC SVM
1. Computing methodologies
  1. Modeling and simulation
    1. Simulation evaluation
2. Software and its engineering
  1. Software creation and management
    1. Designing software
      1. Software implementation planning
        Software design techniques

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ICCMS '18: Proceedings of the 10th International Conference on Computer Modeling and Simulation

January 2018

310 pages

ISBN:9781450363396

DOI:10.1145/3177457

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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University of Canberra: University of Canberra
University of Technology Sydney

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 January 2018

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ICCMS 2018

ICCMS 2018: The 10th International Conference on Computer Modeling and Simulation

January 8 - 10, 2018

Sydney, Australia

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Cited By

Elsayad AMubarak FAbdullah HFahhad MSaad N(2024)Advancements In Passive Landmine Detection A Multiclass Approach With Fluxgate Sensor And Machine Learning Models2024 41st National Radio Science Conference (NRSC)10.1109/NRSC61581.2024.10510535(158-165)Online publication date: 16-Apr-2024
https://doi.org/10.1109/NRSC61581.2024.10510535
Han XPeng J(2023)Bird sound classification based on ECOC-SVMApplied Acoustics10.1016/j.apacoust.2023.109245204(109245)Online publication date: Mar-2023
https://doi.org/10.1016/j.apacoust.2023.109245
Jain KGupta MPatel SAbraham A(2023)Object Classification Using ECOC Multi-class SVM and HOG CharacteristicsIntelligent Systems Design and Applications10.1007/978-3-031-27440-4_3(23-33)Online publication date: 31-May-2023
https://doi.org/10.1007/978-3-031-27440-4_3
Tang YGao SZhuang JHou CHe YChu XMiao ALuo S(2020)Apple Bruise Grading Using Piecewise Nonlinear Curve Fitting for Hyperspectral Imaging DataIEEE Access10.1109/ACCESS.2020.30158088(147494-147506)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3015808
Isaeva ONozhenkova LKulyasov NIsaev S(2020)Graphical Method of Intellectual Simulation Models’ Analysis on the Basis of Technical Systems’ Testing ResultsSoftware Engineering Perspectives in Intelligent Systems10.1007/978-3-030-63319-6_33(368-376)Online publication date: 15-Dec-2020
https://doi.org/10.1007/978-3-030-63319-6_33

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