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Effectiveness of semi-supervised learning in bankruptcy prediction

Stamatis Karlos · Sotiris B. Kotsiantis · Nikos Fazakis · Kyriakos Sgarbas

10 citations0/0 files storedInternational Conference on Information, Intelligence, Systems & Applications (IISA)

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Financial Distress and Bankruptcy Prediction Corporate Insolvency and Governance Data Mining and Machine Learning Applications

Abstract

Adoption of techniques from fields related with Data Science, such as Machine Learning, Data Mining and Predictive Analysis, in the task of bankruptcy prediction can produce useful knowledge for both the policy makers and the organizations that are already funding or are interested in acting towards this direction in the near future. The nature of this task prevents analysts from collecting large amount of data for building accurate predictive models. Semi-supervised algorithms overcome this phenomenon and can perform robust behavior based on a few data. Experiments using data from Greek firms have been made in this work, comparing many semi-supervised schemes against well-known supervised algorithms and the results are promising.

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2005Corporate Financial Distress and Bankruptcy 2006Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review 200535 years of studies on business failure: an overview of the classic statistical methodologies and their related problems 2014A comparative study of classifier ensembles for bankruptcy prediction 2009Comparing four bankruptcy prediction models: Logit, quadratic interval logit, neural and fuzzy neural networks 2013Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring 2012Partial Least Square Discriminant Analysis for bankruptcy prediction 2014An insight into the experimental design for credit risk and corporate bankruptcy prediction systems

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