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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
사건 티켓 분류는 복잡한 시스템 유지 관리에 중요한 역할을 합니다. 그러나 분류 정확도가 낮으면 유지 관리 비용이 높아집니다. 이러한 문제를 해결하기 위해 본 논문에서는 XNUMX클래스 SVM과 일대일, XNUMX과 같은 다중 클래스 SVM 모두에서 구현 가능한 퍼지 출력 지원 벡터 머신(FOSVM) 기반 사건 티켓 분류 접근 방식을 제안합니다. -대-휴식. 우리의 목적은 다중 클래스 SVM의 분류할 수 없는 영역을 해결하여 보다 세밀한 분석을 통해 신뢰할 수 있고 강력한 결과를 출력하는 것입니다. 벤치마크 데이터 세트와 실제 티켓 데이터에 대한 실험에서는 우리 방법이 일반적으로 사용되는 다중 클래스 SVM 및 퍼지 SVM 방법보다 더 나은 성능을 나타냄을 보여줍니다.
Libo YANG
North China University of Water Resources and Electric Power
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부
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
부
@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},}
부
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 -