The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Software defect prediction using a cost sensitive decision forest and voting, and a potential solution to the class imbalance problem
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Citations
Cites
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Cited by
Learning from imbalanced data: open challenges and future directions
Krawczyk, B. (2016) · Progress in Artificial Intelligence · doi:10.1007/s13748-016-0094-0 · Cited by 2,459
Software Defect Prediction Using Ensemble Learning: A Systematic Literature Review
Matloob, F., Ghazal, T. M., Taleb, N., Aftab, S., Ahmad, M., Khan, M. A., Abbas, S., & Soomro, T. R. (2021) · IEEE Access · doi:10.1109/access.2021.3095559 · Cited by 293
An under‐sampled software defect prediction method based on hybrid multi‐objective cuckoo search
Cai, X., Niu, Y., Geng, S., Zhang, J., Cui, Z., Li, J., & Chen, J. (2020) · Concurrency and Computation: Practice and Experience · doi:10.1002/cpe.5478 · Cited by 267
Metadata
- Authors
- Siers, M. J., & Islam, M. Z.
- Venue
- Information Systems
- Publisher
- Elsevier BV
- Published
- 2015-07
- Volume
- 51
- Pages
- 62-71
- ISSN
- 0306-4379 (ISSN-L)
- OpenAlex
- W2086184041
- Cited by
- 165
- References
- 2
- FWCI
- 29.73
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Siers, M. J., & Islam, M. Z. (2015). Software defect prediction using a cost sensitive decision forest and voting, and a potential solution to the class imbalance problem. Information Systems. https://doi.org/10.1016/j.is.2015.02.006
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.
