The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Multiple SVM-RFE for Gene Selection in Cancer Classification With Expression Data
Abstract
This paper proposes a new feature selection method that uses a backward elimination procedure similar to that implemented in support vector machine recursive feature elimination (SVM-RFE). Unlike the SVM-RFE method, at each step, the proposed approach computes the feature ranking score from a statistical analysis of weight vectors of multiple linear SVMs trained on subsamples of the original training data. We tested the proposed method on four gene expression datasets for cancer classification. The results show that the proposed feature selection method selects better gene subsets than the original SVM-RFE and improves the classification accuracy. A Gene Ontology-based similarity assessment indicates that the selected subsets are functionally diverse, further validating our gene selection method. This investigation also suggests that, for gene expression-based cancer classification, average test error from multiple partitions of training and test sets can be recommended as a reference of performance quality.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Wu, Y. (1999) · Technometrics · doi:10.1080/00401706.1999.10485951 · Cited by 27,221
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
A training algorithm for optimal margin classifiers
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992) · Proceedings of the fifth annual workshop on Computational learning theory · doi:10.1145/130385.130401 · Cited by 11,992
Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., et al. (1999) · Science · doi:10.1126/science.286.5439.531 · Cited by 11,516
Cross validatory choice and assessment of statistical predictions
Stone, M. (1974) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1974.tb00994.x · Cited by 10,881
Gene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954
Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling
Alizadeh, A. A., Eisen, M. B., Davis, R. E., Ma, C., Lossos, I. S., Rosenwald, A., Boldrick, J. C., Sabet, H., Tran, T., Yu, X., et al. (2000) · Nature · doi:10.1038/35000501 · Cited by 9,925
Wrappers for feature subset selection
Kohavi, R., & John, G. H. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00043-x · Cited by 8,947
An Introduction to Variable and Feature Selection.
Guyon, I., & Elisseeff, A. (2003) · Cited by 6,576
An Information-Theoretic Definition of Similarity
Lin, D. (1998) · Cited by 3,790
Cited by
Gromski, P. S., Muhamadali, H., Ellis, D. I., Xu, Y., Correa, E., Turner, M. L., & Goodacre, R. (2015) · Analytica Chimica Acta · doi:10.1016/j.aca.2015.02.012 · Cited by 905
Feature selection and analysis on correlated gas sensor data with recursive feature elimination
Yan, K., & Zhang, D. (2015) · Sensors and Actuators B: Chemical · doi:10.1016/j.snb.2015.02.025 · Cited by 516
Schütte, M., Risch, T., Abdavi-Azar, N., Boehnke, K., Schumacher, D., Keil, M., Yildiriman, R., Jandrasits, C., Borodina, T., Amstislavskiy, V., et al. (2017) · Nature Communications · doi:10.1038/ncomms14262 · Cited by 339
SVM-RFE With MRMR Filter for Gene Selection
Mundra, P. A., & Rajapakse, J. C. (2010) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2009.2035284 · Cited by 332
Self-citationFeature selection methods for big data bioinformatics: A survey from the search perspective
Wang, L., Wang, Y., & Chang, Q. (2016) · Methods · doi:10.1016/j.ymeth.2016.08.014 · Cited by 331
Metadata
- Authors
- Duan, K.-B., Rajapakse, J. C., Wang, H., & Azuaje, F.
- Venue
- IEEE Transactions on Nanobioscience
- Published
- 2005-09
- Volume
- 4
- Issue
- 3
- Pages
- 228-234
- ISSN
- 1536-1241 (ISSN-L)
- OpenAlex
- W2103291381
- Cited by
- 468
- References
- 20
- FWCI
- 3.80
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Multiple SVM-RFE for Gene Selection in Cancer Classification With Expression Data. https://app.cochat.ai/papers/10.1109/tnb.2005.853657
- The paper
- Duan, K.-B., Rajapakse, J. C., Wang, H., & Azuaje, F. (2005). Multiple SVM-RFE for Gene Selection in Cancer Classification With Expression Data. IEEE Transactions on Nanobioscience. https://doi.org/10.1109/tnb.2005.853657
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.
