The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
A classificação de tickets de incidentes desempenha um papel importante na manutenção complexa do sistema. No entanto, a baixa precisão da classificação resultará em elevados custos de manutenção. Para resolver esse problema, este artigo propõe uma abordagem de classificação de tickets de incidentes baseada em máquina de vetores de suporte de saída fuzzy (FOSVM), que pode ser implementada no contexto de SVMs de duas classes e SVMs multiclasses, como um contra um e um. -versus-descanso. Nosso objetivo é resolver as regiões não classificáveis de SVMs multiclasse para produzir resultados confiáveis e robustos por meio de análises mais refinadas. Experimentos em conjuntos de dados de benchmark e dados de tickets do mundo real demonstram que nosso método tem melhor desempenho do que os métodos SVM multiclasse e SVM fuzzy comumente usados.
Libo YANG
North China University of Water Resources and Electric Power
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Libo YANG, "Fuzzy Output Support Vector Machine Based Incident Ticket Classification" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 1, pp. 146-151, January 2021, doi: 10.1587/transinf.2020EDP7044.
Abstract: Incident ticket classification plays an important role in the complex system maintenance. However, low classification accuracy will result in high maintenance costs. To solve this issue, this paper proposes a fuzzy output support vector machine (FOSVM) based incident ticket classification approach, which can be implemented in the context of both two-class SVMs and multi-class SVMs such as one-versus-one and one-versus-rest. Our purpose is to solve the unclassifiable regions of multi-class SVMs to output reliable and robust results by more fine-grained analysis. Experiments on both benchmark data sets and real-world ticket data demonstrate that our method has better performance than commonly used multi-class SVM and fuzzy SVM methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDP7044/_p
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@ARTICLE{e104-d_1_146,
author={Libo YANG, },
journal={IEICE TRANSACTIONS on Information},
title={Fuzzy Output Support Vector Machine Based Incident Ticket Classification},
year={2021},
volume={E104-D},
number={1},
pages={146-151},
abstract={Incident ticket classification plays an important role in the complex system maintenance. However, low classification accuracy will result in high maintenance costs. To solve this issue, this paper proposes a fuzzy output support vector machine (FOSVM) based incident ticket classification approach, which can be implemented in the context of both two-class SVMs and multi-class SVMs such as one-versus-one and one-versus-rest. Our purpose is to solve the unclassifiable regions of multi-class SVMs to output reliable and robust results by more fine-grained analysis. Experiments on both benchmark data sets and real-world ticket data demonstrate that our method has better performance than commonly used multi-class SVM and fuzzy SVM methods.},
keywords={},
doi={10.1587/transinf.2020EDP7044},
ISSN={1745-1361},
month={January},}
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TY - JOUR
TI - Fuzzy Output Support Vector Machine Based Incident Ticket Classification
T2 - IEICE TRANSACTIONS on Information
SP - 146
EP - 151
AU - Libo YANG
PY - 2021
DO - 10.1587/transinf.2020EDP7044
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2021
AB - Incident ticket classification plays an important role in the complex system maintenance. However, low classification accuracy will result in high maintenance costs. To solve this issue, this paper proposes a fuzzy output support vector machine (FOSVM) based incident ticket classification approach, which can be implemented in the context of both two-class SVMs and multi-class SVMs such as one-versus-one and one-versus-rest. Our purpose is to solve the unclassifiable regions of multi-class SVMs to output reliable and robust results by more fine-grained analysis. Experiments on both benchmark data sets and real-world ticket data demonstrate that our method has better performance than commonly used multi-class SVM and fuzzy SVM methods.
ER -