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Cited by 202Financial Distress and Bankruptcy PredictionImbalanced Data Classification TechniquesForecasting Techniques and Applications

A comparative study of classifier ensembles for bankruptcy prediction

Tsai, Chih-Fong, Hsu, Yu-Feng, Yen, David C.·2014·Applied Soft Computing
DOI 10.1016/j.asoc.2014.08.047
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1 of 1 references verified0 citation errors · 0 not checkable

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  • Some machine learning algorithms are better for classification than regression, while others are better for regression than classification.

    “However, some are better for classification than regression, while others are vice versa [47,48].”

    From A comprehensive evaluation of ensemble learning for stock-market prediction (2020) · cites this paper as [48]

  • Most machine learning algorithms can be used for both classification and regression tasks.

    “literature shows that most machine learning algorithms can be used for classification and regression tasks.”

    From A comprehensive evaluation of ensemble learning for stock-market prediction (2020) · cites this paper as [48]

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Authors
Tsai, C.-F., Hsu, Y.-F., & Yen, D. C.
Affiliations
  • National Central University (TW)
  • National Sun Yat-sen University (TW)
  • SUNY Oneonta (US)
Corresponding author
Yen, David C.
ORCID
Venue
Applied Soft Computing
Publisher
Elsevier BV
Published
2014-11
Volume
24
Pages
977-984
ISSN
1568-4946 (ISSN-L)
DOI
10.1016/j.asoc.2014.08.047
OpenAlex
W2046792933
Cited by
202
References
1
Citations by year
2026: 12 · 2025: 5 · 2024: 19 · 2023: 17 · 2022: 23 · 2021: 33 · 2020: 28 · 2019: 15 · 2018: 10 · 2017: 19 · 2016: 15 · 2015: 5 · 2014: 1
FWCI
56.68
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
Applied Soft Computing (publishedVersion)
Topics
  • Social Sciences › Business, Management and Accounting › Accounting › Financial Distress and Bankruptcy Prediction
  • Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
Keywords
bankruptcy prediction, ensemble classifiers, boosting, bagging, decision tree ensembles, multilayer perceptron, support vector machine, ensemble size, financial distress prediction

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