The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging
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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
THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY
Heath, M. D., Bowyer, K. W., Kopans, D. B., & Moore, R. M. (2007) · Cited by 1,017
SVMs Modeling for Highly Imbalanced Classification
Tang, Y., Zhang, Y.-Q., Chawla, N. V., & Krasser, S. (2009) · IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2008.2002909 · Cited by 966
Handling imbalanced datasets: A review
Kotsiantis, S. B., Kanellopoulos, D. N., & Pintelas, P. (2006) · Cited by 920
Current Status of the Digital Database for Screening Mammography
Heath, M., Bowyer, K., Kopans, D., Kegelmeyer, P., Moore, R., Chang, K., & Munishkumaran, S. (1998) · Computational Imaging and Vision · doi:10.1007/978-94-011-5318-8_75 · Cited by 787
Experimental perspectives on learning from imbalanced data
Van Hulse, J., Khoshgoftaar, T. M., & Napolitano, A. (2007) · Proceedings of the 24th international conference on Machine learning · doi:10.1145/1273496.1273614 · Cited by 786
A support vector machine approach for detection of microcalcifications
El-Naqa, I., Yang, Y., Wernick, M. N., Galatsanos, N. P., & Nishikawa, R. M. (2002) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2002.806569 · Cited by 571
Computer-aided detection and classification of microcalcifications in mammograms: a survey
Cheng, H. D., Cai, X., Chen, X., Hu, L., & Lou, X. (2003) · Pattern Recognition · doi:10.1016/s0031-3203(03)00192-4 · Cited by 563
Approaches for automated detection and classification of masses in mammograms
Cheng, H. D., Shi, X. J., Min, R., Hu, L. M., Cai, X. P., & Du, H. N. (2006) · Pattern Recognition · doi:10.1016/j.patcog.2005.07.006 · Cited by 538
On the effectiveness of preprocessing methods when dealing with different levels of class imbalance
García, V., Sánchez, J. S., & Mollineda, R. A. (2012) · Knowledge-Based Systems · doi:10.1016/j.knosys.2011.06.013 · Cited by 356
Cited by
Giri, D., Rajendra Acharya, U., Martis, R. J., Vinitha Sree, S., Lim, T.-C., Ahamed, T., & Suri, J. S. (2013) · Knowledge-Based Systems · doi:10.1016/j.knosys.2012.08.011 · Cited by 221
Khandakar, A., Chowdhury, M. E. H., Reaz, M. B. I., Ali, S. H. M., Kiranyaz, S., Rahman, T., Chowdhury, M. H., Ayari, M. A., Alfkey, R., Bakar, A. A. A., et al. (2022) · Sensors · doi:10.3390/s22114249 · Cited by 74
Higher-order spectral analysis of spontaneous speech signals in Alzheimer’s disease
Nasrolahzadeh, M., Mohammadpoory, Z., & Haddadnia, J. (2018) · Cognitive Neurodynamics · doi:10.1007/s11571-018-9499-8 · Cited by 51
Metadata
- Authors
- Ren, J.
- Venue
- Knowledge-Based Systems
- Publisher
- Elsevier BV
- Published
- 2012-02
- Volume
- 26
- Pages
- 144-153
- ISSN
- 0950-7051 (ISSN-L)
- OpenAlex
- W2025025456
- Cited by
- 260
- References
- 30
- FWCI
- 13.60
- Type
- journal article
- Language
- en
- Open access
- green
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- Ren, J. (2012). ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2011.07.016
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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.
