Polina D. Cherniaeva

this article explores the application of rule-based machine learning, specifically decision trees, for predicting corporate bankruptcy within a hierarchical framework. Using a dataset from the Taiwan Economic Journal (1999–2009), the study evaluates the economic performance indicators that critically influence financial distress. The decision tree model achieved an error rate of 1.6%, outperforming neural networks (2.67%) and Kohonen maps (2.98%). The most significant factors identified include Persistent EPS (22.4%), Working Capital to Equity ratio (20.1%), and Pretax Income to Capital (15.5%...