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Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Economics and Business / Faculty of Economics and Business >
Discussion paper >
Series A >
ENSEMBLE NEURAL NETWORK USING A SMALL DATASET FOR THE PREDICTION OF BANKRUPTCY : COMBINING NUMERICAL AND TEXTUAL DATA
| Title: | ENSEMBLE NEURAL NETWORK USING A SMALL DATASET FOR THE PREDICTION OF BANKRUPTCY : COMBINING NUMERICAL AND TEXTUAL DATA |
| Authors: | Rasolomanana, Onjaniaina Mianin’Harizo Browse this author |
| Keywords: | ensemble neural network | | small dataset | | combined data | | bankruptcy prediction |
| Issue Date: | Oct-2021 |
| Publisher: | Faculty of Economics and Business, Hokkaido University |
| Journal Title: | Discussion Paper, Series A |
| Volume: | 361 |
| Start Page: | 1 |
| End Page: | 11 |
| 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. |
| Type: | bulletin (article) |
| URI: | http://hdl.handle.net/2115/82952 |
| Appears in Collections: | Discussion paper > Series A
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