Enhancing Bankruptcy Prediction with Feature Selection in AdaBoost Algorithm

Since the 1960s, bankruptcy prediction research has been intensively examined, with the importance of accurate forecasting representing a nation's economic development. The high costs related to business failures have prompted efforts to enhance prediction techniques, which have been highlighted further as the effects of globalization have increased. There are two primary types of bankruptcy prediction models: statistical and artificial intelligence (AI) methods. The study found that several AI algorithms, namely C5.0, CART, and Support Vector Machines with evolutionary computation, demonstrated efficacy in forecasting impending bankruptcies, as well as short- and long-term predictions. Prior studies have proposed the utilization of Multilayer Perceptron (MLP), ensemble learning methodologies, and AdaBoost as potential approaches for the purpose of bankruptcy prediction. The significance of feature importance is frequently disregarded in such models, resulting in prejudiced outcomes. This study presents a proposed approach for feature selection in AdaBoost algorithm that incorporates the consideration of feature importance, with the aim of enhancing the aforementioned findings. The proposed methodology is validated through a comparative analysis with several other machine learning algorithms. The result showed that ADABoost with feature selection using feature importance generated better value with increased around 5-7% in accuracy score.