IT Ticket Classification: The Simpler, the Better (original) (raw)
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Improving IT Support Efficiency Using AI-Driven Ticket Random Forest Classification Technique
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This research project aims to improve IT support efficiency at Indonesian company XYZ by using AI-based IT support ticket classification integration. This method involved collecting over 1,000 support tickets from the company's IT ticketing system, GLPI, and pre-processing the data to ensure the quality and relevance of the data for analysis. Claims data is enriched with relevant features, including textual information and categorical attributes such as urgency, impact, and requirement expertise. To improve the ticket preference matrix, AI-based language models, especially OpenAI's GPT-3, are used. These templates help to reclassify and improve the work of IT support teams. In addition, the ticket data is used to train the Random Forest classifier, allowing automatic classification of tickets based on their specific characteristics. The performance of the ticket classification system is evaluated using a variety of metrics, and the results are compared with alternative metho...
Comparison of Naive Bayes Method with Support Vector Machine in Helpdesk Ticket Classification
2023
The technical support department or helpdesk department is a unit that requires a quick response in handling its tasks. The company's helpdesk team can consist of several individuals who know specific or specialized issues. Typically, technical problems are handled with an application that can track issues based on tickets. Ticket queue systems are used to facilitate control over the actions of the service or repair provided by the team. Helpdesk applications assist in addressing issues reported by users and then help upper-level management distribute tasks and monitor the helpdesk team's performance, including providing solutions to users' various problems. This research aims to predict the placement of fields that serve assistance based on the corpus users provide in the natural language. Prediction modelling is done using the Naïve Bayes and Support Vector Machine algorithms. The modelling results show that the accuracy rate of helpdesk service prediction with the Naïve Bayes algorithm reaches 82.06%, while the accuracy rate of prediction with the Support Vector Machine algorithm reaches 85.30%.
IAES International Journal of Artificial Intelligence (IJ-AI)
In today's internet world, information technology (IT) ticketing services are potentially increasing across many corporations. Therefore, the automatic classification of IT tickets becomes a significant challenge. Feature selection becomes most important, particularly in data sets with several variables and features. However, enhance classification's precision and performance by stopping insignificant variables. Through our earlier research, we have categorized the unsupervised ticket dataset. As a result, we have converted the dataset into a supervised dataset. In this article, the classification of different IT tickets on Machine learning algorithms, Feature ranking, and feature selection techniques are used to improve the efficiency of machine learning algorithms. However, compared to the machine learning (ML) algorithms, the convolutional neural network (CNN) algorithm provides a better classification of the token IDs and provide better accuracy.
Knowledge Guided Hierarchical Multi-Label Classification Over Ticket Data
IEEE Transactions on Network and Service Management, 2017
Maximal automation of routine IT maintenance procedures is an ultimate goal of IT service management. System monitoring, an effective and reliable means for IT problem detection, generates monitoring ticket. In light of the ticket description, the underlying categories of the IT problem are determined, and the ticket is assigned to the corresponding processing teams for problem resolving. Automatic IT problem category determination acts as a critical part during the routine IT maintenance procedures. In practice, IT problem categories are naturally organized in a hierarchy by specialization. Utilizing the category hierarchy, this paper comes up with a hierarchical multi-label classification method to classify the monitoring tickets. In order to find the most effective classification, a novel contextual hierarchy (CH) loss is introduced in accordance with the problem hierarchy. Consequently, an arising optimization problem is solved by a new greedy algorithm named GLabel. Furthermore, as well as the ticket instance itself, the knowledge from the domain experts, which partially indicates some categories the given ticket may or may not belong to, can also be leveraged to guide the hierarchical multi-label classification. Accordingly, a multi-label inference with the domain expert knowledge is conducted on the basis of the given label hierarchy. The experiment demonstrates the great performance improvement by incorporating the domain knowledge during the hierarchical multi-label classification over the ticket data.
International journal of computer applications, 2024
Due to the numerous issues or challenges that aren't always within the company's control. Customers became unhappy. Customer complaint is the method by which they convey their dissatisfaction. Due to the rapid advancement of technology and the various convenient channels available for customers to voice their complaints, including email, web, and chatbots, online complaints have experienced exponential growth. As a result, classifying these complaints under the pertinent issue in time became a difficult task. Selecting the appropriate classification model and Fitting it with the proper training and testing ratios is a crucial topic that always faces researchers. This paper implements and compares the performance of six text classification machine learning algorithms used in multiclassification (SVM, KNN, NB, DT, RF, and GB) under two types of sampling (random and stratified) with the use of
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A survey on text classification and its applications
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Text classification (a.k.a text categorisation) is an effective and efficient technology for information organisation and management. With the explosion of information resources on the Web and corporate intranets continues to increase, it has being become more and more important and has attracted wide attention from many different research fields. In the literature, many feature selection methods and classification algorithms have been proposed. It also has important applications in the real world. However, the dramatic increase in the availability of massive text data from various sources is creating a number of issues and challenges for text classification such as scalability issues. The purpose of this report is to give an overview of existing text classification technologies for building more reliable text classification applications, to propose a research direction for addressing the challenging problems in text mining.
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Semiautomated Identification and Classification of Customer Complaints
Human Factors and Ergonomics in Manufacturing & Service Industries, 2013
This paper examines the feasibility of extracting useful information from customer comments using a Naïve Bayes classifier. This was done for a database, obtained from a large Korean mobile telephone service provider, of 533 customer calls to call centers in 2009. After eliminating calls not containing customer complaints or comments, the remaining 383 comments were classified by an expert panel into four domains and 27 complaint categories. The four domains were Transaction-related (189 comments, 49%), Product-related (120 comments, 31%), Customer Service or Support-related (38 comments, 10%) and Customer Outreach and Marketing-related (36 comments, 9%). The comments were then randomly assigned to either a training set (257 cases, 67%) or test set (126 cases, 33%). The training set was used to develop a Naïve Bayes classifier that correctly predicted the domain 75% of the time and the specific subcategory 51% of the time for the test set. Prediction accuracy was strongly related to prediction strength for both sets of predictions, suggesting that simple filtering strategies where difficult to understand comments are flagged for expert review and easy comments are automatically classified are both technically feasible and likely to be practically valuable. Several strong predictors were also identified that corresponded to categories more detailed than those originally assigned. C 2012 Wiley Periodicals, Inc.
Ticket Tagger: Machine Learning Driven Issue Classification
2019
Software maintenance is crucial for software projects evolution and success: code should be kept up-to-date and error-free, this with little effort and continuous updates for the end-users. In this context, issue trackers are essential tools for creating, managing and addressing the several (often hundreds of) issues that occur in software systems. A critical aspect for handling and prioritizing issues involves the assignment of labels to them (e.g., for projects hosted on GitHub), in order to determine the type (e.g., bug report, feature request and so on) of each specific issue. Although this labeling process has a positive impact on the effectiveness of issue processing, the current labeling mechanism is scarcely used on GitHub. In this demo, we introduce a tool, called Ticket Tagger, which leverages machine learning strategies on issue titles and descriptions for automatically labeling GitHub issues. Ticket Tagger automatically predicts the labels to assign to issues, with the aim of stimulating the use of labeling mechanisms in software projects, this to facilitate the issue management and prioritization processes. Along with the presentation of the tool's architecture and usage, we also evaluate its effectiveness in performing the issue labeling/classification process, which is critical to help maintainers to keep control of their workloads by focusing on the most critical issue tickets.