Reducing Dimensionality of Variables for a Classification Problem: Fraud Detection

Pedro Shiguihara-Juarez, Nils Murrugarra-Llerena

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Fraud detection can be considered as a classification task since we can use datasets with labelled instances as fraud cases and legal cases. Although, many classifiers were applied to this problem, the data pre-processing related to the reduction of values of each variable is an uncommon approach. We explore a method to reduce the cardinality of the variables in a dataset of fraud transaction to identify improvement in this classification problem. Our best result indicated an improvement of + 31.8% in terms of F1-measure when we reduce the cardinality to detect fraud cases.

Original languageEnglish
Title of host publicationSHIRCON 2019 - 2019 IEEE Sciences and Humanities International Research Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728138183
DOIs
StatePublished - Nov 2019
Externally publishedYes
Event2019 IEEE Sciences and Humanities International Research Conference, SHIRCON 2019 - Lima, Peru
Duration: 13 Nov 201915 Nov 2019

Publication series

NameSHIRCON 2019 - 2019 IEEE Sciences and Humanities International Research Conference

Conference

Conference2019 IEEE Sciences and Humanities International Research Conference, SHIRCON 2019
Country/TerritoryPeru
CityLima
Period13/11/1915/11/19

Keywords

  • Feature Engineering
  • Pre-processing of attributes
  • Reduction of Cardinality of Variables

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