Machine learning models and bankruptcy prediction
Impact in
- Accounting 394
Classified as
- Journal
- Expert Systems with Applications
In The Last Decade
doi.org/10.1016/j.eswa.2017.04.006 →Countries where authors are citing Machine learning models and bankruptcy prediction
This map shows the geographic impact of Machine learning models and bankruptcy prediction. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Machine learning models and bankruptcy prediction with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Machine learning models and bankruptcy prediction more than expected).
Fields of papers citing Machine learning models and bankruptcy prediction
This network shows the impact of Machine learning models and bankruptcy prediction. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Machine learning models and bankruptcy prediction.
About Machine learning models and bankruptcy prediction
This paper, published in 2017, received 568 indexed citations . Written by Flávio Barboza, Herbert Kimura and Edward I. Altman covering the research area of Finance and Accounting. It is primarily cited by scholars working on Accounting (394 citations), Artificial Intelligence (225 citations), Finance (165 citations), Management Science and Operations Research (101 citations) and Economics and Econometrics (91 citations). Published in Expert Systems with Applications.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/10.1016/j.eswa.2017.04.006.