The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction
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Cited by
A systematic review of fundamental and technical analysis of stock market predictions
Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09754-z · Cited by 547
A comprehensive evaluation of ensemble learning for stock-market prediction
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Metadata
- Authors
- Chen, Y., & Hao, Y.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2017-09
- Volume
- 80
- Pages
- 340-355
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2593842564
- Cited by
- 382
- References
- 49
- FWCI
- 24.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Funders
- National Natural Science Foundation of China (E050604/51075306)
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- Chen, Y., & Hao, Y. (2017). A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2017.02.044
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
