使用分类器集成对非结构化IT服务台工单进行分类
Classifying the Unstructured IT Service Desk Tickets Using Ensemble of Classifiers
AI总结：
本文研究利用Bagging、Boosting和Voting三种集成学习方法对非结构化IT服务台工单进行自动分类，并在真实企业数据上验证其相比单一基分类器能提升分类准确率、加快工单解决效率。
摘要：
IT服务台工单的人工分类可能导致工单被路由到错误的解决小组。IT服务台工单的错误分配会导致工单重新分配、不必要的资源利用，并延迟解决时间。传统的机器学习算法可用于自动分类IT服务台工单。可以通过挖掘历史的非结构化工单描述及相应标签来训练服务台工单分类器模型。然后，该模型可用于根据工单描述对新服务台工单进行分类。通过使用各种分类集成技术，可以进一步提高传统分类器系统的性能。本文介绍了三种最流行的集成方法，即Bagging、Boosting和Voting集成，用于组合来自不同模型的预测，以进一步提高工单分类器系统的准确性。使用各种性能指标，将集成分类器系统与各个基分类器进行性能对比。与相应的基分类器相比，分类器集成表现良好。构建这种自动化工单分类器系统的优势包括简化用户界面、更快的解决时间、提高生产力、客户满意度和业务增长。来自大型企业IT基础设施的真实世界服务台工单数据被用于我们的研究目的。
英文摘要：
Manual classification of IT service desk tickets may result in routing of the tickets to the wrong resolution group. Incorrect assignment of IT service desk tickets leads to reassignment of tickets, unnecessary resource utilization and delays the resolution time. Traditional machine learning algorithms can be used to automatically classify the IT service desk tickets. Service desk ticket classifier models can be trained by mining the historical unstructured ticket description and the corresponding label. The model can then be used to classify the new service desk ticket based on the ticket description. The performance of the traditional classifier systems can be further improved by using various ensemble of classification techniques. This paper brings out the three most popular ensemble methods ie, Bagging, Boosting and Voting ensemble for combining the predictions from different models to further improve the accuracy of the ticket classifier system. The performance of the ensemble classifier system is checked against the individual base classifiers using various performance metrics. Ensemble of classifiers performed well in comparison with the corresponding base classifiers. The advantages of building such an automated ticket classifier systems are simplified user interface, faster resolution time, improved productivity, customer satisfaction and growth in business. The real world service desk ticket data from a large enterprise IT infrastructure is used for our research purpose.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
