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Ensemble methods for strongly imbalanced data: bankruptcy prediction | IEEE Conference Publication | IEEE Xplore

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Ensemble methods for strongly imbalanced data: bankruptcy prediction


Abstract:

Application of the machine learning methods on strongly imbalanced datasets is a challenging task in the field of data processing. Imbalanced learning is part of many rea...Show More

Abstract:

Application of the machine learning methods on strongly imbalanced datasets is a challenging task in the field of data processing. Imbalanced learning is part of many real-world applications and it is a very vivid research area. Moreover, bankruptcy prediction, even though it is one of the most popular prediction applications, is still not successfully solved. In this paper, we present a comparison of several ensemble machine learning methods applied on a recently acquired dataset of small and medium-sized enterprises operating in the Slovak Republic. The highest achieved prediction accuracy of the proposed classification models, measured by geometric mean, is in some cases almost 100%. Results are validated on three datasets from different business areas, namely agriculture, construction and retails.
Date of Conference: 12-14 September 2019
Date Added to IEEE Xplore: 09 June 2020
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ISSN Information:

Conference Location: Subotica, Serbia

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