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On the Usage of Feature Ranking and Selection Techniques to classify Heterogenous I.T. Ticketing Data


M Venakata Subbarao, Kasukurty Venkatarao, Dr Suresh Chittineni, Subhadra Kompella


Vol. 22  No. 11  pp. 405-412


In today's internet world, I.T. ticketing services are potentially increasing across many corporations. Therefore, the automatic classification of I.T. tickets has become 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 I.T. support systems. 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 I.T. tickets. Machine learning algorithms such as Support Vector Machine (SVM), Gaussian Na?ve Bayesian, Decision Trees, logistics regression, and K.N.N. were used. In addition, we have used Feature ranking and feature selection techniques to improve the efficiency of Machine Learning algorithms.


Machine Learning, Incident Response, Text Mining, Support Vector Machine, Deep Learning.