Evaluating Active Learning Methods for Bankruptcy Prediction

Georgios Kostopoulos, Stamatis Karlos, Sotiris Kotsiantis, Vassilis Tampakas

The prediction of corporate bankruptcy has been addressed as an increasingly important financial problem and has been extensively analyzed in the accounting literature. Over recent years, several machine learning methods have been effectively applied to build accurate predictive models for detecting business failure with remarkable results, such as neural networks (NNs) and ensemble methods. This paper investigates the effectiveness of the active learning framework to predict bankruptcy using financial data from a set of Greek firms. Active learning is an emerging subfield of machine learning exploiting a small amount of labeled data together with a large pool of unlabeled data to improve learning accuracy. From what we know so far there exists no study dealing with the implementation of active learning methodologies in the financial field. Several experiments take place in our research comparing the accuracy measures of familiar active learners and demonstrating their efficiency in contrast to representative supervised methods.

The final publication is available at Springer via https://doi.org/10.1007/978-3-319-67615-9_5.