Title
Effective Bankruptcy Prediction Models for North American Companies
Document Type
Book Chapter
Keywords
bankruptcy; prediction models; North American companies; undersampling; oversampling; balanced accuracy
Identifier Data
10.4018/978-1-7998-9220-5.ch108
Publisher
IGI Global
Publication Source
Encyclopedia of Data Science and Machine Learning
Rights Management
© 2023
Abstract
Bankruptcy prediction is a widely researched topic. However, few studies focus on dealing with the imbalance problem. This article proposes a new technique that applies a bagging undersampling procedure to balance the data and compares it to random undersampling and five oversampling techniques. The performance of the algorithm is evaluated by a random forest's balanced accuracy, sensitivity, and specificity. The results show that models trained after applying the oversampling techniques are prone to overfitting, and the model trained after applying the proposed method had the highest balanced accuracy without overfitting.
