Gene Selection for Cancer Classification using Support Vector Machines

Support-vector networks

Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421

Support-Vector Networks

Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565

Statistical Learning Theory

Wu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236

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

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

Untitled work

(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

Molecular classification of cancer: class discovery and class prediction by gene expression monitoring

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

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

Technologies for Clinical Diagnosis Using Expired Human Breath Analysis

Mathew, T. L., Pownraj, P., Abdulla, S., & Pullithadathil, B. (2015) · Diagnostics · doi:10.3390/diagnostics5010027 · Cited by 149

Automatic driver sleepiness detection using EEG, EOG and contextual information

Barua, S., Ahmed, M. U., Ahlstrom, C. H. G., & Begum, S. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.07.054 · Cited by 149

Toward Data‐Driven Applications in Lithium‐Ion Battery Cell Manufacturing

Turetskyy, A., Thiede, S., Thomitzek, M., von Drachenfels, N., Pape, T., & Herrmann, C. A. (2019) · Energy Technology · doi:10.1002/ente.201900136 · Cited by 149

Prediction of sediment heavy metal at the Australian Bays using newly developed hybrid artificial intelligence models

Bhagat, S. K., Tiyasha, T., Awadh, S. M., Tung, T. M., Jawad, A. H., & Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Z. M. (2020) · Environmental Pollution · doi:10.1016/j.envpol.2020.115663 · Cited by 149

Predicting Diagnostic Gene Biomarkers Associated With Immune Infiltration in Patients With Acute Myocardial Infarction

Zhao, E., Xie, H., & Zhang, Y. (2020) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2020.586871 · Cited by 149

Generalized spectral bounds for sparse LDA

Moghaddam, B., Weiss, Y., & Avidan, S. (2006) · International Conference on Machine Learning · doi:10.1145/1143844.1143925 · Cited by 149

The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence

Watson, D. (2019) · Minds and Machines · doi:10.1007/s11023-019-09506-6 · Cited by 148

Comparison between geostatistical and machine learning models as predictors of topsoil organic carbon with a focus on local uncertainty estimation

Veronesi, F., & Schillaci, C. (2019) · Ecological Indicators · doi:10.1016/j.ecolind.2019.02.026 · Cited by 148

A meta-learning approach to automatic kernel selection for support vector machines

Ali, S., & Smith‐Miles, K. (2006) · Neurocomputing · doi:10.1016/j.neucom.2006.03.004 · Cited by 148

Single-cell senescence identification reveals senescence heterogeneity, trajectory, and modulators

Tao, W., Yu, Z., & Han, J.-D. J. (2024) · Cell Metabolism · doi:10.1016/j.cmet.2024.03.009 · Cited by 147

Robust Feature Selection for Microarray Data Based on Multicriterion Fusion

Yang, F., & Mao, K. (2010) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2010.103 · Cited by 147

Transfer Learning for Molecular Cancer Classification Using Deep Neural Networks

Sevakula, R. K., Singh, V., Verma, N. K., Kumar, C., & Cui, Y. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2822803 · Cited by 147

Total Variation Regularization for fMRI-Based Prediction of Behavior

Michel, V., Gramfort, A., Varoquaux, G. P., Eger, E., & Thirion, B. (2011) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2113378 · Cited by 146

Feature clustering based support vector machine recursive feature elimination for gene selection

Huang, X., Zhang, L., Wang, B., Li, F., & Zhang, Z. (2017) · Applied Intelligence · doi:10.1007/s10489-017-0992-2 · Cited by 146

A survey on artificial intelligence approaches for medical image classification

Deepa, S. N. (2011) · Indian Journal of Science and Technology · doi:10.17485/ijst/2011/v4i11.35 · Cited by 145

Soil Organic Carbon Mapping Using Multispectral Remote Sensing Data: Prediction Ability of Data with Different Spatial and Spectral Resolutions

Žížala, D., Minařík, R., & Zádorová, T. (2019) · Remote Sensing · doi:10.3390/rs11242947 · Cited by 145

Finding and removing Clever Hans: Using explanation methods to debug and improve deep models

Anders, C. J., Weber, L., Neumann, D., Samek, W., Müller, K., & Lapuschkin, S. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.07.015 · Cited by 145

Application of irregular and unbalanced data to predict diabetic nephropathy using visualization and feature selection methods

Cho, B. H., Yu, H., Kim, K., Kim, T. H., Kim, I. Y., & Kim, S. I. (2007) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2007.09.005 · Cited by 145

Sample Handling for Mass Spectrometric Proteomic Investigations of Human Sera

West-Nielsen, M., Høgdall, E. V. S., Marchiori, E., Høgdall, C. K., Schou, C., & Heegaard, N. H. H. (2005) · Analytical Chemistry · doi:10.1021/ac050253g · Cited by 145

Insights into multimodal imaging classification of ADHD

Colby, J. B., Rudie, J. D., Brown, J. A., Douglas, P. K., Cohen, M. S., & Shehzad, Z. (2012) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2012.00059 · Cited by 144

Feature Selection via Coalitional Game Theory

Cohen, S., Dror, G., & Ruppin, E. (2007) · Neural Computation · doi:10.1162/neco.2007.19.7.1939 · Cited by 144

Random Forest in Clinical Metabolomics for Phenotypic Discrimination and Biomarker Selection

Chen, T., Cao, Y., Zhang, Y., Liu, J., Bao, Y., Wang, C., Jia, W., & Zhao, A. (2013) · Evidence-based Complementary and Alternative Medicine · doi:10.1155/2013/298183 · Cited by 144

A protocol for building and evaluating predictors of disease state based on microarray data

Wessels, L. F. A., Reinders, M. J. T., Hart, A. A. M., Veenman, C. J., Dai, H., He, Y. D., & Veer, L. V. ‘. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti429 · Cited by 144

EEG-Based Prediction of Epileptic Seizures Using Phase Synchronization Elicited from Noise-Assisted Multivariate Empirical Mode Decomposition

Cho, D., Min, B., Kim, J., & Lee, B. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2618937 · Cited by 144

Machine Learning-Based Predictive Modeling of Postpartum Depression

Shin, D., Lee, K. J., Adeluwa, T., & Hur, J. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9092899 · Cited by 144

Hybrid particle swarm optimization and tabu search approach for selecting genes for tumor classification using gene expression data

Shen, Q., Shi, W., & Kong, W. (2008) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2007.10.001 · Cited by 144

Literature Review on Feature Selection Methods for High-Dimensional Data

Singh, D. A. A. G., Appavu, S., & Leavline, E. J. (2016) · International Journal of Computer Applications · doi:10.5120/ijca2016908317 · Cited by 143

XGBFEMF: An XGBoost-Based Framework for Essential Protein Prediction

Zhong, J., Sun, Y., Peng, W., Xie, M., Yang, J., & Tang, X. (2018) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2018.2842219 · Cited by 143

Subset selection by Pareto optimization

Qian, C., Yu, Y., & Zhou, Z. (2015) · Cited by 142

Feature Selection Based on High Dimensional Model Representation for Hyperspectral Images

Taşkın, G., Kaya, H., & Bruzzone, L. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2687128 · Cited by 142

Utilization of machine learning for prediction of post-traumatic stress: a re-examination of cortisol in the prediction and pathways to non-remitting PTSD

Galatzer‐Levy, I. R., Ma, S., Statnikov, A., Yehuda, R., & Shalev, A. Y. (2017) · Translational Psychiatry · doi:10.1038/tp.2017.38 · Cited by 142

Machine learning applications for prediction of relapse in childhood acute lymphoblastic leukemia

Pan, L., Liu, G., Lin, F., Zhong, S., Xia, H., Sun, X., & Liang, H. (2017) · Scientific Reports · doi:10.1038/s41598-017-07408-0 · Cited by 142

A competition on generalized software-based face presentation attack detection in mobile scenarios

Boulkenafet, Z., Komulainen, J., Akhtar, Z., Benlamoudi, A., Samai, D., Bekhouche, S. E., Ouafi, A., Dornaika, F., Ahmed, A. T., Qin, L., et al. (2017) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/btas.2017.8272758 · Cited by 142

Field-level crop yield estimation with PRISMA and Sentinel-2

Marshall, M., Belgiu, M., Boschetti, M., Pepe, M., Stein, A., & Nelson, A. D. (2022) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.03.008 · Cited by 142

Metabonomic analysis of saliva reveals generalized chronic periodontitis signature

Aimetti, M., Cacciatore, S., Graziano, A., & Tenori, L. (2011) · Metabolomics · doi:10.1007/s11306-011-0331-2 · Cited by 141

A Novel Feature Selection Algorithm using Particle Swarm Optimization for Cancer Microarray Data

Sahu, B., & Mishra, D. (2012) · Procedia Engineering · doi:10.1016/j.proeng.2012.06.005 · Cited by 141

Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning

Formisano, E., Martino, F. D., & Valente, G. (2008) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2008.01.052 · Cited by 141

Towards Ultrahigh Dimensional Feature Selection for Big Data

Tan, M., Tsang, I. W.-H., & Wang, L. (2012) · DR-NTU (Nanyang Technological University) · doi:10.5555/2627435.2638579 · Cited by 141

Data mining in lithium-ion battery cell production

Schnell, J., Nentwich, C., Endres, F., Kollenda, A., Distel, F., Knoche, T., & Reinhart, G. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.12.062 · Cited by 141

Assessing Autonomic Function from Electrodermal Activity and Heart Rate Variability During Cold-Pressor Test and Emotional Challenge

Ghiasi, S., Greco, A., Barbieri, R., Scilingo, E. P., & Valenza, G. (2020) · Scientific Reports · doi:10.1038/s41598-020-62225-2 · Cited by 141

Gene expression profile based classification models of psoriasis

Guo, P., Luo, Y., Mai, G., Zhang, M., Wang, G., Zhao, M., Gao, L., Li, F., & Zhou, F. (2013) · Genomics · doi:10.1016/j.ygeno.2013.11.001 · Cited by 140

Remote Sensing Image Processing

Camps‐Valls, G., Tuia, D., Gómez‐Chova, L., Jiménez, S., & Malo, J. (2012) · Synthesis lectures on image, video, and multimedia processing · doi:10.1007/978-3-031-02247-0 · Cited by 140

Tensor-Based Classification Models for Hyperspectral Data Analysis

Makantasis, K., Doulamis, A. D., Doulamis, N. D., & Nikitakis, A. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2845450 · Cited by 140

Recursive feature elimination in random forest classification supports nanomaterial grouping

Bahl, A., Hellack, B., Balaș, M., Dinischiotu, A., Wiemann, M., Brinkmann, J. F. F., Luch, A., Renard, B. Y., & Haase, A. (2019) · NanoImpact · doi:10.1016/j.impact.2019.100179 · Cited by 140

The Art of Feature Engineering

Duboue, P. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671682 · Cited by 140

Accurate Detection and Recognition of Dirty Vehicle Plate Numbers for High-Speed Applications

Panahi, R., & Gholampour, I. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2586520 · Cited by 139

Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data

Maros, M. E., Capper, D., Jones, D., Hovestadt, V., von Deimling, A., Pfister, S. M., Benner, A., Zucknick, M., & Sill, M. (2020) · Nature Protocols · doi:10.1038/s41596-019-0251-6 · Cited by 139

Efficient Machine Learning Models for Early Stage Detection of Autism Spectrum Disorder

Bala, M., Ali, M. H., Satu, M. S., Hasan, K. F., & Moni, M. A. (2022) · Algorithms · doi:10.3390/a15050166 · Cited by 139

Protein function classification via support vector machine approach

Cai, C. Z., Wang, W., Sun, L., & Chen, Y. (2003) · Mathematical Biosciences · doi:10.1016/s0025-5564(03)00096-8 · Cited by 139

Methods Towards Invasive Human Brain Computer Interfaces

Lal, T. N., Hinterberger, T., Widman, G., Schröder, M., Hill, N. J., Rosenstiel, W., Elger, C. E., Schölkopf, B., Birbaumer, N., Saul, L. K., et al. (2005) · Cited by 139

Application of support vector machines for T-cell epitopes prediction

Zhao, Y., Pinilla, C., Valmori, D., Martinꝉ, R., & Simon, R. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg255 · Cited by 139

Consensus group stable feature selection

Loscalzo, S., Yu, L., & Ding, C. H. Q. (2009) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1557019.1557084 · Cited by 138

On-board wet road surface identification using tyre/road noise and Support Vector Machines

Alonso, J. I., López, J. M., Pavón, I., Recuero, M., Asensio, C., de Arcas, G., & Bravo, A. (2013) · Applied Acoustics · doi:10.1016/j.apacoust.2013.09.011 · Cited by 138

Feature Selection with Linked Data in Social Media

Tang, J., & Liu, H. (2012) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972825.11 · Cited by 138

A genetic algorithm based wrapper feature selection method for classification of hyperspectral images using support vector machine

Zhuo, L., Zheng, J., Li, X., Wang, F., Ai, B., & Qian, J. (2008) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.813256 · Cited by 138

Non-invasive diagnosis of non-alcoholic steatohepatitis and fibrosis with the use of omics and supervised learning: a proof of concept study

Perakakis, N., Polyzos, S. A., Yazdani, A., Sala-Vila, A., Kountouras, J., Anastasilakis, A. D., & Mantzoros, C. S. (2019) · Metabolism · doi:10.1016/j.metabol.2019.154005 · Cited by 137

Risk classification of cancer survival using ANN with gene expression data from multiple laboratories

Chen, Y., Ke, W.-C., & Chiu, H. (2014) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2014.02.006 · Cited by 137

A novel ensemble machine learning for robust microarray data classification

Peng, Y. (2005) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2005.04.001 · Cited by 137

Feature subset selection Filter–Wrapper based on low quality data

Figueredo, J. M. C., Garrido, M. C., & Martínez‐España, R. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.05.051 · Cited by 137

Detection of Parkinson’s disease based on voice patterns ranking and optimized support vector machine

Lahmiri, S., & Shmuel, A. (2018) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2018.08.029 · Cited by 137

Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine Learning

Li, X., Zhang, S., Xu, L., & Hong, X. (2020) · Angewandte Chemie International Edition · doi:10.1002/anie.202000959 · Cited by 137

An efficient statistical feature selection approach for classification of gene expression data

Chandra, B., & Gupta, M. (2011) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2011.01.001 · Cited by 136

Novel cell segmentation and online SVM for cell cycle phase identification in automated microscopy

Wang, M., Zhou, X., Li, F., Huckins, J. F., King, R. W., & Wong, S. T. C. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm530 · Cited by 136

Robust, low-cost, non-intrusive sensing and recognition of seated postures

Mutlu, B., Krause, A., Forlizzi, J., Guestrin, C., & Hodgins, J. K. (2007) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/1294211.1294237 · Cited by 136

Self-taught dimensionality reduction on the high-dimensional small-sized data

Zhu, X., Huang, Z. H., Yang, Y., Shen, H. T., Xu, C., & Luo, J. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.07.018 · Cited by 135

Chemometrics and qualitative analysis have a vibrant relationship

Szymańska, E., Gerretzen, J., Engel, J., Geurts, B., Blanchet, L., & Buydens, L. M. C. (2015) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2015.02.015 · Cited by 135

A hybrid gene selection approach for microarray data classification using cellular learning automata and ant colony optimization

Sharbaf, F. V., Mosafer, S., & Moattar, M. H. (2016) · Genomics · doi:10.1016/j.ygeno.2016.05.001 · Cited by 135

Estimating defoliation of Scots pine stands using machine learning methods and vegetation indices of Sentinel-2

Hawryło, P., Bednarz, B., Wężyk, P., & Szostak, M. (2018) · European Journal of Remote Sensing · doi:10.1080/22797254.2017.1417745 · Cited by 135

Functional connectivity-based classification of autism and control using SVM-RFECV on rs-fMRI data

Wang, C., Xiao, Z., & Wu, J. (2019) · Physica Medica · doi:10.1016/j.ejmp.2019.08.010 · Cited by 135

Feature Subset Selection for Support Vector Machines Through Discriminative Function Pruning Analysis

Mao, K. (2004) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2002.805808 · Cited by 135

Cancer classification and prediction using logistic regression with Bayesian gene selection

Zhou, X., Liu, K., & Wong, S. T. C. (2004) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2004.07.009 · Cited by 135

Developing a Multi-Dose Computational Model for Drug-Induced Hepatotoxicity Prediction Based on Toxicogenomics Data

Su, R., Wu, H., Xu, B., Liu, X., & Wei, L. (2019) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2858756 · Cited by 134

A review of the stability of feature selection techniques for bioinformatics data

Awada, W., Khoshgoftaar, T. M., Dittman, D. J., Wald, R., & Napolitano, A. (2012) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri.2012.6303031 · Cited by 134

Estimating Grassland Biomass Using SVM Band Shaving of Hyperspectral Data

Clevers, J. G. P. W., van der Heijden, G. W. A. M., Verzakov, S., & Schaepman, M. E. (2007) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.73.10.1141 · Cited by 134

The Fisher-Markov Selector: Fast Selecting Maximally Separable Feature Subset for Multiclass Classification with Applications to High-Dimensional Data

Cheng, Q. S., Zhou, H., & Cheng, J. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.195 · Cited by 134

Comparison of Deep Transfer Learning Strategies for Digital Pathology

Mormont, R., Geurts, P., & Marée, R. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00303 · Cited by 134

Kernel-Based Domain-Invariant Feature Selection in Hyperspectral Images for Transfer Learning

Persello, C., & Bruzzone, L. (2015) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2015.2503885 · Cited by 133

Feature selection for outcome prediction in oesophageal cancer using genetic algorithm and random forest classifier

Desbordes, P., Ruan, S., Modzelewski, R., Vauclin, S., Véra, P., & Gardin, I. (2016) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2016.12.002 · Cited by 133

Histone Interaction Landscapes Visualized by Crosslinking Mass Spectrometry in Intact Cell Nuclei

Fasci, D., van Ingen, H., Scheltema, R. A., & Heck, A. J. R. (2018) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra118.000924 · Cited by 133

Downscaling MODIS land surface temperature over a heterogeneous area: An investigation of machine learning techniques, feature selection, and impacts of mixed pixels

Ebrahimy, H., & Azadbakht, M. (2019) · Computers & Geosciences · doi:10.1016/j.cageo.2019.01.004 · Cited by 133

Treelets—An adaptive multi-scale basis for sparse unordered data

Lee, A. B., Nadler, B., & Wasserman, L. (2008) · The Annals of Applied Statistics · doi:10.1214/07-aoas137 · Cited by 132

Multiple Kernel Learning in the Primal for Multimodal Alzheimer’s Disease Classification

Liu, F., Zhou, L., Shen, C., & Yin, J. (2013) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2013.2285378 · Cited by 132

Feature selection for support vector machines with RBF kernel

Liu, Q., Chen, C., Zhang, Y., & Hu, Z. (2011) · Artificial Intelligence Review · doi:10.1007/s10462-011-9205-2 · Cited by 132

Comparing the learning effectiveness of BP, ELM, I-ELM, and SVM for corporate credit ratings

Zhong, H., Miao, C., Shen, Z., & Feng, Y. (2013) · Neurocomputing · doi:10.1016/j.neucom.2013.02.054 · Cited by 132

Support vector machine classification on the web

Pavlidis, P., Wapinski, I., & Noble, W. S. (2004) · Bioinformatics · doi:10.1093/bioinformatics/btg461 · Cited by 132

Novel feature selection methods to financial distress prediction

Lin, F., Liang, D., Yeh, C.-C., & Huang, J. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.09.047 · Cited by 131

Multi-Task Deep Model With Margin Ranking Loss for Lung Nodule Analysis

Liu, L., Dou, Q., Chen, H., Qin, J., & Heng, P. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2934577 · Cited by 131

Exhaled Volatile Organic Compounds of Infection: A Systematic Review

Ahmed, W., Lawal, O., Nijsen, T. M. E., Goodacre, R., & Fowler, S. J. (2017) · ACS Infectious Diseases · doi:10.1021/acsinfecdis.7b00088 · Cited by 130

Spatial prediction of soil water retention in a Páramo landscape: Methodological insight into machine learning using random forest

Blanco, C. M. G., Gómez, V. M. B., Crespo, P., & Ließ, M. (2017) · Geoderma · doi:10.1016/j.geoderma.2017.12.002 · Cited by 130

Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques

Kim, S. H., & Boukouvala, F. (2019) · Optimization Letters · doi:10.1007/s11590-019-01428-7 · Cited by 130

Performance Evaluation of a Proposed Machine Learning Model for Chronic Disease Datasets Using an Integrated Attribute Evaluator and an Improved Decision Tree Classifier

Mishra, S., Mallick, P. K., Tripathy, H. K., Bhoi, A. K., & González‐Briones, A. (2020) · Applied Sciences · doi:10.3390/app10228137 · Cited by 130

Graph convolution network with similarity awareness and adaptive calibration for disease-induced deterioration prediction

Song, X., Zhou, F., Frangi, A. F., Cao, J., Xiao, X., Lei, Y., Wang, T., & Lei, B. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101947 · Cited by 130

Feature selection with kernelized multi-class support vector machine

Guo, Y., Zhang, Z., & Tang, F. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.107988 · Cited by 130

Blind Kriging: Implementation and performance analysis

Couckuyt, I., Forrester, A. I. J., Gorissen, D., Turck, F. D., & Dhaene, T. (2012) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2012.03.002 · Cited by 129

A 15-gene signature for prediction of colon cancer recurrence and prognosis based on SVM

Xu, G., Zhang, M., Zhu, H., & Xu, J. (2016) · Gene · doi:10.1016/j.gene.2016.12.016 · Cited by 129

Mapping soil organic carbon stocks and trends with satellite-driven high resolution maps over South Africa

Venter, Z. S., Hawkins, H., Cramer, M. D., & Mills, A. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.145384 · Cited by 129

Ovarian cancer detection from metabolomic liquid chromatography/mass spectrometry data by support vector machines

Guan, W., Zhou, M., Hampton, C. Y., Benigno, B. B., Walker, L. D., Gray, A., McDonald, J. F., & Fernández, F. M. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-259 · Cited by 129

A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets

Lai, C., Reinders, M. J. T., Veer, L. J. V., & Wessels, L. F. A. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-235 · Cited by 129

Unsupervised Feature Selection for Multi-View Data in Social Media

Tang, J., Hu, X., Gao, H., & Liu, H. (2013) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972832.30 · Cited by 128

Feature selection and tumor classification for microarray data using relaxed Lasso and generalized multi-class support vector machine

Kang, C., Huo, Y., Xin, L., Tian, B., & Yu, B. (2018) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2018.12.010 · Cited by 128

Submitted to Machine Learning. Summary

Guyon, I., Weston, J., & Barnhill, S. D. (2008)

Gene selection and classification for microarray data of colon and normal tissues using genetic algorithms

He, A. (2007) · Computer Engineering and Applications Journal

Gene Extraction for Cancer Diagnosis by Support Vector Machines

Huang, T.-M., & Kecman, V. (2005) · Lecture notes in computer science · doi:10.1007/11550822_96 · Cited by 15

Gene selection using genetic algorithm and support vectors machines

Shutao Li, A., Wu, X., & Hu, X. (2008) · Soft Computing · doi:10.1007/s00500-007-0251-2 · Cited by 64

Gene Selection for Cancer Classification Using Relevance Vector Machine

Zhang, W., & Liu, J. (2007) · International Conference on Bioinformatics and Biomedical Engineering · doi:10.1109/icbbe.2007.50 · Cited by 1