The DOI matches its Crossref record; 47 of the 48 references it cites were found in a registry or an index.
Gene Selection for Cancer Classification using Support Vector Machines
Open accessOverview
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The data structure is captured by this efficient sparse coding model, which also finds correlations between different input vectors [92].”
From Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree (2023) · cites this paper as [92]
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
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Self-citationCortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565
Self-citationWu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236
Self-citationCluster 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
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855
(1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895
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
Self-citationGolub, 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
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 9,096
Research works printed but not linked: 2
- Ghigna, C., Moroni, M., Porta, C., Riva, I., & Biamonti, G. (1998). Altered expression of heterogeneous nuclear ribonucleoproteins and SR factors in human. Cancer Research, 58, 5818-5824.
- LeCun, Y., Denker, J. S., & Solla, S. A. (1990). Optimum brain damage. In D. Touretzky (Ed.). Advances in neural information processing systems 2 (pp. 598-605). San Mateo, CA: Morgan Kaufmann.
Cited by
Sorted by citations
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,543
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
A review of feature selection techniques in bioinformatics
Saeys, Y., Inza, I., & Larrañaga, P. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm344 · Cited by 5,389
A survey on feature selection methods
Chandrashekar, G., & Sahin, F. (2014) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2013.11.024 · Cited by 5,189
What is a support vector machine?
Noble, W. S. (2006) · Nature Biotechnology · doi:10.1038/nbt1206-1565 · Cited by 4,450
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023) · ACM Computing Surveys · doi:10.1145/3560815 · Cited by 4,361
Biau, G., & Scornet, E. (2016) · Test · doi:10.1007/s11749-016-0481-7 · Cited by 4,149
SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty
de Sousa, L. M., Poggio, L., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., & Rossiter, D. G. (2021) · SOIL · doi:10.5194/soil-7-217-2021 · Cited by 2,776
InteractiVenn: a web-based tool for the analysis of sets through Venn diagrams
Heberle, H., Meirelles, G. V., da Silva, F. R., Telles, G. P., & Minghim, R. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0611-3 · Cited by 2,700
Variable selection using random forests
Genuer, R., Poggi, J.-M., & Tuleau-Malot, C. (2010) · Pattern Recognition Letters · doi:10.1016/j.patrec.2010.03.014 · Cited by 2,619
Feature Selection: A Data Perspective
Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., & Liu, H. (2018) · ACM Computing Surveys · doi:10.1145/3136625 · Cited by 2,442
An extensive empirical study of feature selection metrics for text classification
Forman, G. (2003) · doi:10.5555/944919.944974 · Cited by 2,400
Feature selection in machine learning: A new perspective
Cai, J., Luo, J., Wang, S., & Yang, S. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.11.077 · Cited by 2,175
CatBoost for big data: an interdisciplinary review
Hancock, J., & Khoshgoftaar, T. M. (2020) · Journal Of Big Data · doi:10.1186/s40537-020-00369-8 · Cited by 2,023
Machine learning algorithm validation with a limited sample size
Vabalas, A., Gowen, E., Poliakoff, E., & Casson, A. J. (2019) · PLOS ONE · doi:10.1371/journal.pone.0224365 · Cited by 1,901
Selection bias in gene extraction on the basis of microarray gene-expression data
Ambroise, C., & McLachlan, G. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.102102699 · Cited by 1,537
Efficient and Robust Feature Selection via Joint ℓ2,1-Norms Minimization
Nie, F., Huang, H., Cai, X., & Ding, C. H. Q. (2010) · Cited by 1,522
A GA-based feature selection and parameters optimizationfor support vector machines
Huang, C.-L., & Wang, C.-J. (2005) · Expert Systems with Applications · doi:10.1016/j.eswa.2005.09.024 · Cited by 1,382
A survey of feature selection and feature extraction techniques in machine learning
Khalid, S., Khalil, T., & Nasreen, S. (2014) · Science and Information Conference · doi:10.1109/sai.2014.6918213 · Cited by 1,315
Machine Learning for High-Throughput Stress Phenotyping in Plants
Singh, A., Ganapathysubramanian, B., Singh, A. K., & Sarkar, S. (2015) · Trends in Plant Science · doi:10.1016/j.tplants.2015.10.015 · Cited by 1,190
Correlation and variable importance in random forests
Gregorutti, B., Michel, B., & Saint-Pierre, P. (2016) · Statistics and Computing · doi:10.1007/s11222-016-9646-1 · Cited by 1,189
Understanding Random Forests: From Theory to PracticePreprint
Louppe, G. (2014) · arXiv (Cornell University) · doi:10.13140/2.1.1570.5928 · Cited by 1,101
Selecting critical features for data classification based on machine learning methods
Chen, R.-C., Dewi, C., Huang, S.-W., & Caraka, R. E. (2020) · Journal Of Big Data · doi:10.1186/s40537-020-00327-4 · Cited by 1,089
Cao, K. L., Boitard, S., & Besse, P. C. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-253 · Cited by 1,042
Combining SVMs with Various Feature Selection Strategies
Chen, Y., & Lin, C. (2008) · Studies in fuzziness and soft computing · doi:10.1007/978-3-540-35488-8_13 · Cited by 1,019
Feature selection for classification: A review
Tang, J., Alelyani, S., & Liu, H. (2014) · doi:10.1201/b17320-7 · Cited by 997
Weston, J., Mukherjee, S., Chapelle, O., Pontil, M., Poggio, T., & Vapnik, V. N. (2000) · Cited by 988
Self-citationA Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data
Hira, Z. M., & Gillies, D. F. (2015) · Advances in Bioinformatics · doi:10.1155/2015/198363 · Cited by 978
Chen, W., Xie, X., Wang, J., Pradhan, B., Hong, H., Bui, D. T., Duan, Z., & Ma, J. (2016) · CATENA · doi:10.1016/j.catena.2016.11.032 · Cited by 971
Gromski, P. S., Muhamadali, H., Ellis, D. I., Yun Xu, A., Correa, E. S., Turner, M. L., & Goodacre, R. (2015) · Analytica Chimica Acta · doi:10.1016/j.aca.2015.02.012 · Cited by 953
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Arbabshirani, M. R., Plis, S., Sui, J., & Calhoun, V. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.079 · Cited by 912
Sentiment analysis in multiple languages
Abbasi, A., Chen, H., & Salem, A. (2008) · ACM Transactions on Information Systems · doi:10.1145/1361684.1361685 · Cited by 904
Using machine learning approaches for multi-omics data analysis: A review
Reel, P. S., Reel, S., Pearson, E., Trucco, E., & Jefferson, E. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107739 · Cited by 903
Zacharaki, E. I., Wang, S., Chawla, S., Yoo, D. S., Wolf, R. L., Melhem, E. R., & Davatzikos, C. A. (2009) · Magnetic Resonance in Medicine · doi:10.1002/mrm.22147 · Cited by 880
A User’s Guide to Support Vector Machines
Ben‐Hur, A., & Weston, J. (2009) · Methods in molecular biology · doi:10.1007/978-1-60327-241-4_13 · Cited by 879
Self-citationA review of feature selection methods in medical applications
Remeseiro, B., & Bolón‐Canedo, V. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103375 · Cited by 851
Statnikov, A., Aliferis, C., Tsamardinos, I., Hardin, D. P., & Levy, S. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bti033 · Cited by 845
Franke, K., Ziegler, G., Klöppel, S., & Gaser, C. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.01.005 · Cited by 836
SVM-RFE: selection and visualization of the most relevant features through non-linear kernels
Sanz, H., Valim, C., Vegas, E., Oller, J. M., & Reverter, F. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2451-4 · Cited by 835
Feature Selection for Classification of Hyperspectral Data by SVM
Pal, M., & Foody, G. M. (2010) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2009.2039484 · Cited by 832
A review of feature selection methods on synthetic data
Bolón-Canedo, V., Sánchez-Maroño, N., & Alonso-Betanzos, A. (2013) · Knowledge and Information Systems · doi:10.1007/s10115-012-0487-8 · Cited by 830
Credit scoring with a data mining approach based on support vector machines
Huang, C.-L., Chen, M., & Wang, C.-J. (2006) · Expert Systems with Applications · doi:10.1016/j.eswa.2006.07.007 · Cited by 830
Comparative cellular analysis of motor cortex in human, marmoset and mouse
Bakken, T. E., Jorstad, N. L., Hu, Q., Lake, B. B., Tian, W., Kalmbach, B. E., Crow, M., Hodge, R. D., Krienen, F. M., Sorensen, S. A., et al. (2021) · Nature · doi:10.1038/s41586-021-03465-8 · Cited by 812
1-norm Support Vector Machines
Zhu, J., Rosset, S., Tibshirani, R. J., & Hastie, T. J. (2003) · Cited by 803
AI applications to medical images: From machine learning to deep learning
Castiglioni, I., Rundo, L., Codari, M., Leo, G. D., Salvatore, C., Interlenghi, M., Gallivanone, F., Cozzi, A., D’Amico, N. C., & Sardanelli, F. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.02.006 · Cited by 779
Multicategory Support Vector Machines
Lee, Y., Lin, Y., & Wahba, G. (2004) · Journal of the American Statistical Association · doi:10.1198/016214504000000098 · Cited by 766
Pediatric acute lymphoblastic leukemia
Inaba, H., & Mullighan, C. G. (2020) · Haematologica · doi:10.3324/haematol.2020.247031 · Cited by 765
Data-analysis strategies for image-based cell profiling
Caicedo, J. C., Cooper, S., Heigwer, F., Warchal, S., Qiu, P., Molnar, C., Vasilevich, A. S., Barry, J. D., Bansal, H. S., Kraus, O., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4397 · Cited by 739
Support Vector Machines and Kernels for Computational Biology
Ben‐Hur, A., Ong, C. S., Sonnenburg, S., Schölkopf, B., & Rätsch, G. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000173 · Cited by 726
A methodological approach to the classification of dermoscopy images
Celebi, M. E., Kingravi, H. A., Uddin, B., Iyatomi, H., Aslandogan, Y. A., Stoecker, W. V., & Moss, R. H. (2007) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2007.01.003 · Cited by 713
Machine learning pipeline for battery state-of-health estimation
Roman, D., Saxena, S., Robu, V., PECHT, M. G., & Flynn, D. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00312-3 · Cited by 713
Robust Feature Selection Using Ensemble Feature Selection Techniques
Saeys, Y., Abeel, T., & de Peer, Y. V. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-87481-2_21 · Cited by 706
Statnikov, A., Wang, L., & Aliferis, C. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-319 · Cited by 701
Use of the zero norm with linear models and kernel methods
Weston, J., Elisseeff, A., Schölkopf, B., & Tipping, M. (2003) · MPG.PuRe (Max Planck Society) · doi:10.5555/944919.944982 · Cited by 692
Self-citationZhang, D., & Shen, D. G. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.09.069 · Cited by 689
Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products
Granitto, P. M., Furlanello, C., Franco Biasioli, A., & Gasperi, F. (2006) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2006.01.007 · Cited by 678
A review of microarray datasets and applied feature selection methods
Bolón‐Canedo, V., Sánchez‐Maroño, N., Alonso‐Betanzos, A., Benítez, J. M., & Herrera, F. (2014) · Information Sciences · doi:10.1016/j.ins.2014.05.042 · Cited by 676
Chicco, D., & Jurman, G. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-1023-5 · Cited by 664
Stability of feature selection algorithms: a study on high-dimensional spaces
Kalousis, A., Prados, J., & Hilario, M. (2006) · Knowledge and Information Systems · doi:10.1007/s10115-006-0040-8 · Cited by 642
Darst, B. F., Malecki, K. M. C., & Engelman, C. D. (2018) · BMC Genetics · doi:10.1186/s12863-018-0633-8 · Cited by 641
Clarke, R. C., Ressom, H. W., Wang, A., Xuan, J., Liu, M., Gehan, E. A., & Wang, Y. (2007) · Nature reviews. Cancer · doi:10.1038/nrc2294 · Cited by 623
Uncertainty in Big Data Analytics: Survey, Opportunities, and Challenges
Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019) · Journal Of Big Data · doi:10.1186/s40537-019-0206-3 · Cited by 620
A survey on filter techniques for feature selection in gene expression microarray analysis
Lazar, C., Taminau, J., Meganck, S., Steenhoff, D., Coletta, A., Molter, C., de Schaetzen, V., Duque, R., Bersini, H., & Nowe, A. (2012) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2012.33 · Cited by 619
Variable selection using svm based criteria
Rakotomamonjy, A. (2003) · doi:10.5555/944919.944977 · Cited by 616
Survey on SVM and their application in image classification
Chandra, M. A., & Bedi, S. S. (2018) · International Journal of Information Technology · doi:10.1007/s41870-017-0080-1 · Cited by 613
Benchmark and Survey of Automated Machine Learning Frameworks
Zöller, M.-A., & Huber, M. F. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11854 · Cited by 612
Critical assessment of automated flow cytometry data analysis techniques
Aghaeepour, N., Finak, G., Hoos, H. H., Mosmann, T. R., Brinkman, R. R., Gottardo, R., & Scheuermann, R. H. (2013) · Nature Methods · doi:10.1038/nmeth.2365 · Cited by 607
Smith, G. (2018) · Journal Of Big Data · doi:10.1186/s40537-018-0143-6 · Cited by 607
A Sparse PLS for Variable Selection when Integrating Omics Data
Cao, K. L., Rossouw, D., Robert-Granié, C., & Besse, P. C. (2008) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1390 · Cited by 604
Miao, J., & Niu, L. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.07.111 · Cited by 602
A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine
Cao, L., Chua, K. S., Chong, W. K., Lee, H. P., & Gu, Q. (2003) · Neurocomputing · doi:10.1016/s0925-2312(03)00433-8 · Cited by 595
Robust biomarker identification for cancer diagnosis with ensemble feature selection methods
Abeel, T., Helleputte, T., de Peer, Y. V., Dupont, P., & Saeys, Y. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp630 · Cited by 585
SLEP: Sparse Learning with Efficient Projections
Liu, J., Ji, S., & Ye, J. (2011) · Cited by 569
Feng, X., Li, Q., Zhu, Y., Hou, J., Jin, L., & Wang, J. (2015) · Atmospheric Environment · doi:10.1016/j.atmosenv.2015.02.030 · Cited by 563
A Review of Feature Reduction Techniques in Neuroimaging
Mwangi, B., Tian, T. S., & Soares, J. C. (2014) · Neuroinformatics · doi:10.1007/s12021-013-9204-3 · Cited by 559
Human–Agent Teaming for Multirobot Control: A Review of Human Factors Issues
Chen, J. Y. C., & Barnes, M. J. (2014) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2013.2293535 · Cited by 553
Polymer Genome: A Data-Powered Polymer Informatics Platform for Property Predictions
Kim, C., Chandrasekaran, A. R., Huan, T. D., Das, D., & Ramprasad, R. (2018) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.8b02913 · Cited by 549
Machine learning in medical applications: a review of state-of-the-art methods
Shehab, M., Abualigah, L., Shambour, Q., Abu-Hashem, M. A., Shambour, M. K. Y., Alsalibi, A. I., & Gandomi, A. H. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105458 · Cited by 542
Feature selection and analysis on correlated gas sensor data with recursive feature elimination
Yan, K., & Zhang, D. D. (2015) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2015.02.025 · Cited by 536
A Benchmark for Interpretability Methods in Deep Neural NetworksPreprint
Hooker, S., Erhan, D., Kindermans, P.-J., & Kim, B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.10758 · Cited by 531
Ateniese, G., Mancini, L. V., Spognardi, A., Villani, A., Vitali, D., & Felici, G. (2015) · International Journal of Security and Networks · doi:10.1504/ijsn.2015.071829 · Cited by 518
Support Vector Channel Selection in BCI
Lal, T., Schroder, M., Hinterberger, T., Weston, J., Bogdan, M., Birbaumer, N., & Schölkopf, B. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.827827 · Cited by 516
Self-citationMartino, F. D., Valente, G., Staeren, N., Ashburner, J., Goebel, R., & Formisano, E. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.06.037 · Cited by 514
BCI competition III: Dataset II—Ensemble of SVMs for BCI P300 speller
Rakotomamonjy, A., & Guigue, V. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.915728 · Cited by 512
Guido, R., Ferrisi, S., Lofaro, D., & Conforti, D. (2024) · Information · doi:10.3390/info15040235 · Cited by 509
EEG Based Emotion Recognition: A Tutorial and Review
Li, X., Zhang, Y., Tiwari, P., Song, D., Hu, B., Yang, M., Zhao, Z., Kumar, N., & Marttinen, P. (2023) · ACM Computing Surveys · doi:10.1145/3524499 · Cited by 508
From explanations to feature selection: assessing SHAP values as feature selection mechanism
Marcilio, W. E., & Eler, D. M. (2020) · 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi51738.2020.00053 · Cited by 504
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2021) · arXiv · doi:10.48550/arxiv.2107.13586 · Cited by 4,352
Markov blanket-embedded genetic algorithm for gene selection
Zhu, Z., Ong, Y.-S., & Dash, M. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2007.02.007 · Cited by 495
Supervised classification of human microbiota
Knights, D., Costello, E. K., & Knight, R. D. (2010) · FEMS Microbiology Reviews · doi:10.1111/j.1574-6976.2010.00251.x · Cited by 494
A wrapper method for feature selection using Support Vector Machines
Maldonado, S., & Weber, R. W. (2009) · Information Sciences · doi:10.1016/j.ins.2009.02.014 · Cited by 486
Aliferis, C., Statnikov, A., Tsamardinos, I., Mani, S., & Koutsoukos, X. (2010) · doi:10.5555/1756006.1756013 · Cited by 483
PyMVPA: a Python Toolbox for Multivariate Pattern Analysis of fMRI Data
Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V., & Pollmann, S. (2009) · Neuroinformatics · doi:10.1007/s12021-008-9041-y · Cited by 482
Minimum redundancy maximum relevance feature selection approach for temporal gene expression data
Radovic, M. D., Ghalwash, M. F., Filipović, N., & Zoran Obradovic, A. (2017) · BMC Bioinformatics · doi:10.1186/s12859-016-1423-9 · Cited by 479
Discriminative Least Squares Regression for Multiclass Classification and Feature Selection
Xiang, S., Nie, F., Meng, G., Pan, C., & Zhang, C. (2012) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2012.2212721 · Cited by 476
Multiple SVM-RFE for Gene Selection in Cancer Classification With Expression Data
Duan, K., Rajapakse, J. C., Wang, H., & Azuaje, F. J. (2005) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2005.853657 · Cited by 474
Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis
Li, W., Prasad, S., Fowler, J. E., & Bruce, L. M. (2011) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2011.2165957 · Cited by 472
Huang, X., & Zhang, L. (2012) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2012.2202912 · Cited by 460
Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis
Wang, M., & Chen, H. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105946 · Cited by 459
Recent advances in the development of gene delivery systems.
Sung, Y., & Kim, S. (2019) · Biomaterials Research · doi:10.1186/s40824-019-0156-z · Cited by 458
Metadata
- Authors
- Guyon, I., Weston, J., Barnhill, S., & Vapnik, V.
- Affiliations
- Georgia Institute of Technology (US)
- AT&T (United States) (US)
- Corresponding author
- Guyon, Isabelle
- Vapnik, Vladimir
- ORCID
- Guyon, Isabelle
- Venue
- Machine Learning
- Published
- 2002-01
- Volume
- 46
- Issue
- 1-3
- Pages
- 389-422
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2143426320
- Cited by
- 9,954
- References
- 48
- Citations by year
- 2026: 553 · 2025: 676 · 2024: 564 · 2023: 586 · 2022: 576 · 2021: 695 · 2020: 607 · 2019: 571 · 2018: 488 · 2017: 478 · 2016: 408 · 2015: 444 · 2014: 400 · 2013: 400 · 2012: 359
- FWCI
- 38.69
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- Funders
- Delft Research Centres
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- gene selection, cancer classification, DNA microarrays, gene expression data, recursive feature elimination, support vector machine, leukemia, colon cancer, gene redundancy, genetic diagnosis, drug discovery
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