The goal of this study is to compare the behaviour of three ensemble methods (AdaBoost, Bagging and Random Forest) in a financial application. Since the sixties several classification techniques have been used for corporate failure prediction. In last decades, the interest in this task has widely increased owing to the existence of data bases with available information.

Classification trees are a powerful alternative to the more traditional statistical models. This model has the advantage of being able to detect non-linear relationships and showing a good performance in presence of qualitative information as it happens in corporate failure prediction problems. As a result, they are widely used as base classifiers for ensemble methods.

AdaBoost constructs its base classifiers in sequence, updating a distribution over the training examples to create each base classifier. Bagging combines the individual classifiers built in bootstrap replicates of the training set. Random Forest is a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest.

In this paper, we compare the prediction accuracy of these techniques for the corporate failure prediction task.

Key Words: Bankruptcy Prediction, AdaBoost, Bagging, Random Forest.

• Legal notice:
  

  Copyright (c) Pielaszek Research, all rights reserved.
  The above materials, including auxiliary resources, are subject to Publisher's copyright and the Author(s) intellectual rights. Without limiting Author(s) rights under respective Copyright Transfer Agreement, no part of the above documents may be reproduced without the express written permission of Pielaszek Research, the Publisher. Express permission from the Author(s) is required to use the above materials for academic purposes, such as lectures or scientific presentations.
  In every case, proper references including Author(s) name(s) and URL of this webpage: https://science24.com/paper/10655 must be provided.