1. Corporate bankruptcy prediction has become an increasingly important issue for financial institutions due to recent financial crises.
2. A new and improved Boosting, FS-Boosting, is proposed to predict corporate bankruptcy by injecting feature selection strategy into Boosting.
3. Experimental results reveal that FS-Boosting can be used as an alternative method for the corporate bankruptcy prediction.
The article provides a comprehensive overview of the current state of corporate bankruptcy prediction and proposes a new and improved boosting technique, FS-Boosting, as an alternative method for predicting corporate bankruptcy. The article is well written and provides a clear explanation of the proposed technique and its potential benefits. The authors also provide two real world datasets to demonstrate the effectiveness of their proposed technique.
However, there are some potential biases in the article that should be noted. Firstly, the authors do not provide any evidence or data to support their claims about the effectiveness of their proposed technique compared to other existing techniques such as ANNs, DTs, CBRs, SVMs etc., which could lead to one-sided reporting or unsupported claims in favor of their own technique. Secondly, the authors do not explore any possible counterarguments or risks associated with using their proposed technique which could lead to partiality in reporting or missing points of consideration when evaluating its effectiveness. Finally, while the authors provide two real world datasets for testing purposes they do not discuss how representative these datasets are or whether they are sufficient for making general conclusions about the effectiveness of their proposed technique which could lead to missing evidence for the claims made in the article.
In conclusion, while this article provides a comprehensive overview of corporate bankruptcy prediction and proposes a new and improved boosting technique as an alternative method for predicting corporate bankruptcy it does have some potential biases that should be noted when evaluating its trustworthiness and reliability.