Artificial Intelligence Classification for IT Ticketing Data Using Improved Feature Analysis Techniques

Abstract

IT ticketing services are potentially increasing across many corporations in today's internet world. 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. This Automation in unsupervised ticket classification is a massive impediment to improving the IT support systems. This article the classification of different IT tickets. Through our earlier research, we have categorized the unsupervised ticket dataset. As a result, we have converted the dataset into a supervised dataset. Machine learning algorithms such as Support Vector Machine (SVM), Gaussian Naïve Bayesian, Decision Trees, logistic regression, KNN, and CNN were used. In addition, we have used Feature ranking and feature selection techniques to improve the efficiency of Machine Learning algorithms. However, compared to the ML algorithms, the DL algorithms, like the CNN algorithm, provide a better classification of the token IDs and better accuracy, which is discussed in detail in the results and discussion.