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IT Ticket Classification: The Simpler, the Better
- Source :
- IEEE Access, Vol 8, Pp 193380-193395 (2020)
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- Recently, automatic classification of IT tickets has gained notable attention due to the increasing complexity of IT services deployed in enterprises. There are multiple discussions and no general opinion in the research and practitioners' community on the design of IT ticket classification tasks, specifically the choice of ticket text representation techniques and classification algorithms. Our study aims to investigate the core design elements of a typical IT ticket text classification pipeline. In particular, we compare the performance of TF-IDF and linguistic features-based text representations designed for ticket complexity prediction. We apply various classifiers, including kNN, its enhanced versions, decision trees, naìˆve Bayes, logistic regression, support vector machines, as well as semi-supervised techniques to predict the ticket class label of low, medium, or high complexity. Finally, we discuss the evaluation results and their practical implications. As our study shows, linguistic representation not only proves to be highly explainable but also demonstrates a substantial prediction quality increase over TF-IDF. Furthermore, our experiments evidence the importance of feature selection. We indicate that even simple algorithms can deliver high-quality prediction when using appropriate linguistic features.
- Subjects :
- text classification
General Computer Science
Computer science
Decision tree
02 engineering and technology
Machine learning
computer.software_genre
IT tickets
Naive Bayes classifier
process complexity
400 Sprache::410 Linguistik::410 Linguistik
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
General Materials Science
000 Informatik, Informationswissenschaft, allgemeine Werke::000 Informatik, Wissen, Systeme::004 Datenverarbeitung
Informatik
business.industry
General Engineering
linguistics
TF-IDF
Class (biology)
Statistical classification
machine learning
Ticket
020201 artificial intelligence & image processing
Artificial intelligence
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
computer
lcsh:TK1-9971
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....a6cd33d44b090e0e4c2ca9696886e4a2