Bankruptcy prediction using Extreme Learning Machine and financial expertise

  • Yu Q
  • Miche Y
  • Séverin E
 et al. 
  • 51

    Readers

    Mendeley users who have this article in their library.
  • 51

    Citations

    Citations of this article.

Abstract

Bankruptcy prediction has been widely studied as a binary classification problem using financial ratios methodologies. In this paper, Leave-One-Out-Incremental Extreme Learning Machine (LOO-IELM) is explored for this task. LOO-IELM operates in an incremental way to avoid inefficient and unnecessary calculations and stops automatically with the neurons of which the number is unknown. Moreover, Combo method and further Ensemble model are investigated based on different LOO-IELM models and the specific financial indicators. These indicators are chosen using different strategies according to the financial expertise. The entire process has shown its good performance with a very fast speed, and also helps to interpret the model and the special ratios. © 2013.

Author-supplied keywords

  • Bankruptcy Prediction
  • Extreme Learning Machine
  • Incremental Learning
  • Leave-One-Out

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Get full text

Authors

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free