ENSEMBLE NEURAL NETWORK USING A SMALL DATASET FOR THE PREDICTION OF BANKRUPTCY: COMBINING NUMERICAL AND TEXTUAL DATA

Onjaniaina Mianin'Harizo Rasolomanana

No 361, Discussion paper series. A from Graduate School of Economics and Business Administration, Hokkaido University

Abstract: This paper presents an ensemble neural network using a small data set in the context of bankruptcy prediction. The individual models of the ensemble use different data of different types. We compare the performance of three neural network models: one using a single type of data, one using a combination of both data in a single data frame, and one using ensemble learning. The results show that the ensemble model outperformed the individual model and the combined model. This suggests that with scarce training data, especially when using different types of data, ensemble neural network can improve the level of prediction accuracy.

Keywords: ensemble neural network; small dataset; combined data; bankruptcy prediction (search for similar items in EconPapers)
Pages: 11 pages
Date: 2021-10
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:hok:dpaper:361

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