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.
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 -