Effectiveness of semi-supervised learning in bankruptcy prediction

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...

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Bibliographic Details
Published in2016 7th International Conference on Information, Intelligence, Systems & Applications (IISA) pp. 1 - 6
Main Authors Karlos, Stamatis, Fazakis, Nikos, Kotsiantis, Sotiris, Sgarbas, Kyrgiakos
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2016
Subjects
Algorithm design and analysis
Bankruptcy
bankruptcy prediction
Data mining
labeled ratio
Prediction algorithms
predictive analysis
Predictive models
semi-supervised schemes
Training
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Summary: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.
DOI:10.1109/IISA.2016.7785435