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The impact of distance, feature weighting and selection for KNN in credit default prediction
University of Skövde, School of Informatics.
2020 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

With the rapid spread of credit card business around the world, credit risk has also expanded dramatically. The occurrence of a large number of credit cardcustomer defaults has caused huge losses to financial institutions such as banks. Therefore, it is particularly important to accurately identify default customers. We investigate the use of the K Nearest Neighbor (KNN) algorithm, to evaluate the impact of the alternative distance functions, feature weighting, and feature selection on the accuracy and the area under curve (AUC) of the credit card default prediction model. For our evaluation, we use a credit card user dataset from Taiwan. We find that the Mahalanobis distance function performed best, feature weighting, and feature selection could improve the accuracy and AUC of the model.

Place, publisher, year, edition, pages
2020. , p. 37
National Category
Information Systems, Social aspects
Identifiers
URN: urn:nbn:se:his:diva-18655OAI: oai:DiVA.org:his-18655DiVA, id: diva2:1448036
Subject / course
Informationsteknologi
Educational program
Data Science - Master’s Programme
Supervisors
Examiners
Available from: 2020-06-26 Created: 2020-06-26 Last updated: 2020-06-26Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • apa-cv
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf