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Using conformal prediction for multi-label document classification in e-Mail support systems
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0002-8929-7220
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0002-9316-4842
Telenor Sverige AB, SWE.
2019 (English)In: Lect. Notes Comput. Sci., Springer Verlag , 2019, Vol. 11536, p. 308-322Conference paper, Published paper (Refereed)
Abstract [en]

For any corporation the interaction with its customers is an important business process. This is especially the case for resolving various business-related issues that customers encounter. Classifying the type of such customer service e-mails to provide improved customer service is thus important. The classification of e-mails makes it possible to direct them to the most suitable handler within customer service. We have investigated the following two aspects of customer e-mail classification within a large Swedish corporation. First, whether a multi-label classifier can be introduced that performs similarly to an already existing multi-class classifier. Second, whether conformal prediction can be used to quantify the certainty of the predictions without loss in classification performance. Experiments were used to investigate these aspects using several evaluation metrics. The results show that for most evaluation metrics, there is no significant difference between multi-class and multi-label classifiers, except for Hamming loss where the multi-label approach performed with a lower loss. Further, the use of conformal prediction did not introduce any significant difference in classification performance for neither the multi-class nor the multi-label approach. As such, the results indicate that conformal prediction is a useful addition that quantifies the certainty of predictions without negative effects on the classification performance, which in turn allows detection of statistically significant predictions. © Springer Nature Switzerland AG 2019.

Place, publisher, year, edition, pages
Springer Verlag , 2019. Vol. 11536, p. 308-322
Series
Lecture Notes in Computer Science ; 11536
Keywords [en]
Conformal prediction, Customer support e-mail, Multi-label classification, Electronic mail, Forecasting, Information retrieval systems, Intelligent systems, Sales, Classification performance, Conformal predictions, Customer support, Document Classification, Email classification, Evaluation metrics, Multi label classification, Multi-class classifier, Classification (of information)
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-18592DOI: 10.1007/978-3-030-22999-3_28Scopus ID: 2-s2.0-85068624865ISBN: 9783030229986 (print)OAI: oai:DiVA.org:bth-18592DiVA, id: diva2:1349335
Conference
International Conference on Computational Science, ICCS, Faro, Algarve, 9 July 2019 through 11 July 2019
Available from: 2019-09-09 Created: 2019-09-09 Last updated: 2019-09-20Bibliographically approved

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Borg, AntonBoldt, Martin

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf