Hybrid classifier for predicting financial distress

Arian Dhini, Naufal Allaam Aji, Harjani Rezkya Putri, Dhea Indriyanti

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

Abstract

The challenges and competition in the investment world recently became a great focus to be studied as it is greatly linked to profitability. Business sustainability has become a great issue to ensure the profit generated and keep the operation going, reducing the risk of financial distress of a firm. Model to predict the financially distressed firm has been developed over decades, using conventional statistical and recent data mining methods. A combination of clustering and classification methods called hybrid classifier, used in this paper. Cluster analysis, using k-means, conduct as the pre-classification. The clustering result used to construct the classification model into two clusters. From the cluster analysis, 17% of overall data clustered into cluster 1, meanwhile, 83% into cluster 2. The clustering results later used as the output label to the classification phase. The result shows a great overall accuracy of 98.6111% from the logistic regression and C4.5 decision tree completed with boosting, with an AUC value of 0.996 and 0.990 classified as excellent classification.

Original languageEnglish
Title of host publication2019 16th International Conference on Service Systems and Service Management, ICSSSM 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728119410
DOIs
Publication statusPublished - Jul 2019
Event16th International Conference on Service Systems and Service Management, ICSSSM 2019 - Shenzhen, China
Duration: 13 Jul 201915 Jul 2019

Publication series

Name2019 16th International Conference on Service Systems and Service Management, ICSSSM 2019

Conference

Conference16th International Conference on Service Systems and Service Management, ICSSSM 2019
CountryChina
CityShenzhen
Period13/07/1915/07/19

Keywords

  • Data mining
  • Decision tree
  • Financial distress
  • Financial distress prediction
  • Hybrid classifier
  • Indonesia Stock Exchange (IDX)
  • K-means clustering
  • Logistic regression
  • Support vector machine

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