Abstract

The previous research related to financial statements audit mostly used single-label classification, such as opinion prediction, opinion identification, and opinion detection. We propose the use of multi-label classification to predict the 'opinion and exceptions' using data from financial statements audit reports in Central Kalimantan province. We use financial ratios as attributes as well as opinion and exceptions as labels. In this research, we use three of Problem Transformation Methods, namely Binary Relevance (BR), Classifier Chains (CC) and Random k-labelsets (RAkEL), where each of will be combined with three of base classifiers such as J48, SMO, and Random Forest. The best evaluation metrics results for Hamming Loss is 0.19, for One-Error is 0.253, for Rank Loss is 0.16, and for Average Precision is 0.793.

Original languageEnglish
Title of host publication2018 International Conference on Information and Communications Technology, ICOIACT 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages747-752
Number of pages6
ISBN (Electronic)9781538609545
DOIs
Publication statusPublished - 26 Apr 2018
Event1st International Conference on Information and Communications Technology, ICOIACT 2018 - Yogyakarta, Indonesia
Duration: 6 Mar 20187 Mar 2018

Publication series

Name2018 International Conference on Information and Communications Technology, ICOIACT 2018
Volume2018-January

Conference

Conference1st International Conference on Information and Communications Technology, ICOIACT 2018
Country/TerritoryIndonesia
CityYogyakarta
Period6/03/187/03/18

Keywords

  • audit reports
  • financial ratios
  • financial statements
  • multi-label classification
  • problem transformation methods

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