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

ADMET Evaluation in Drug Discovery. 18. Reliable Prediction of Chemical-Induced Urinary Tract Toxicity by Boosting Machine Learning Approaches

Lei, T., Sun, H., Kang, Y., Zhu, F., Liu, H., Zhou, W., Wang, Z., Li, D., Li, Y., & Hou, T. (2017) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.7b00631 · Cited by 90

Mapping fractional woody cover in semi-arid savannahs using multi-seasonal composites from Landsat data

Higginbottom, T. P., Symeonakis, E., Meyer, H., & van der Linden, S. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.02.010 · Cited by 90

Convolutional Neural Network Based on Bandwise-Independent Convolution and Hard Thresholding for Hyperspectral Band Selection

Feng, J., Chen, J., Sun, Q., Shang, R., Cao, X., Zhang, X., & Jiao, L. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.3000725 · Cited by 90

Multi-site clustering and nested feature extraction for identifying autism spectrum disorder with resting-state fMRI

Wang, N., Yao, D., Ma, L., & Liu, M. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102279 · Cited by 90

A Smart Home Energy Management System Utilizing Neurocomputing-Based Time-Series Load Modeling and Forecasting Facilitated by Energy Decomposition for Smart Home Automation

Lin, Y., Tang, H.-S., Shen, T.-Y., & Hsia, C. (2022) · IEEE Access · doi:10.1109/access.2022.3219068 · Cited by 90

Cancer recognition with bagged ensembles of support vector machines

Valentini, G., Muselli, M., & Ruffino, F. (2003) · Neurocomputing · doi:10.1016/j.neucom.2003.09.001 · Cited by 90

Transcription and Separation of Drum Signals From Polyphonic Music

Gillet, O., & Gaël Richard, A. (2008) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2007.914120 · Cited by 90

Data mining for case-based reasoning in high-dimensional biological domains

Arshadi, N., & Jurišica, I. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.124 · Cited by 90

A cross-modal genetic framework for the development and plasticity of sensory pathways

Frangeul, L., Pouchelon, G., Telley, L., Lefort, S., Lüscher, C., & Jabaudon, D. (2016) · Nature · doi:10.1038/nature19770 · Cited by 90

Texture analysis of cardiac cine magnetic resonance imaging to detect nonviable segments in patients with chronic myocardial infarction

Larroza, A., López-Lereu, M. P., Monmeneu, J. V., Gavara, J., Chorro, F. J., Bodí, V., & Moratal, D. (2018) · Medical Physics · doi:10.1002/mp.12783 · Cited by 90

Absolute cosine-based SVM-RFE feature selection method for prostate histopathological grading

Sahran, S., Albashish, D., Abdullah, A., Shukor, N. A., & Pauzi, S. H. M. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2018.04.002 · Cited by 90

Learning STRIPS Operators from Noisy and Incomplete ObservationsPreprint

Mourão, K., Zettlemoyer, L. S., Petrick, R. P. A., & Steedman, M. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1210.4889 · Cited by 90

Evolution of cellular diversity in primary motor cortex of human, marmoset monkey, 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. (2020) · doi:10.1101/2020.03.31.016972 · Cited by 809

Diagnosis of autism spectrum disorders using regional and interregional morphological features: Diagnosis of ASD Using Morphological Features

Wee, C.-Y., Wang, L., Shi, F., Yap, P.-T., & Shen, D. (2014) · Human Brain Mapping · doi:10.1002/hbm.22411 · Cited by 89

An Analysis of Domestic Abuse Discourse on Reddit

Schrading, N., Alm, C. O., Ptucha, R., & Homan, C. M. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1309 · Cited by 89

A fully automated artificial intelligence method for non-invasive, imaging-based identification of genetic alterations in glioblastomas

Calabrese, E., Villanueva-Meyer, J. E., & Cha, S. (2020) · Scientific Reports · doi:10.1038/s41598-020-68857-8 · Cited by 89

Interpretation of Depression Detection Models via Feature Selection Methods

Alghowinem, S., Gedeon, T., Goecke, R., Cohn, J. F., & Parker, G. B. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.3035535 · Cited by 89

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

Zhao, X., Kim, J., Warns, K., Wang, X., Ramuhalli, P., Çetiner, S. M., Kang, H. G., & Golay, M. W. (2021) · Frontiers in Energy Research · doi:10.3389/fenrg.2021.696785 · Cited by 89

A Novel Machine Learning-Based Methodology for Tool Wear Prediction Using Acoustic Emission Signals

Ferrando, J. L., Barrena, T. F. D., García, A., de Buruaga, M. S., Badiola, X., & Vicente, J. (2021) · Sensors · doi:10.3390/s21175984 · Cited by 89

A gene signature for breast cancer prognosis using support vector machine

Xu, X., Zhang, Y., Zou, L., Wang, M., & Li, A. (2012) · International Conference on Biomedical Engineering and Informatics (BMEI) · doi:10.1109/bmei.2012.6513032 · Cited by 89

An unsupervised hierarchical dynamic self-organizing approach to cancer class discovery and marker gene identification in microarray data

Hsu, A. L., Tang, S., & Halgamuge, S. K. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg296 · Cited by 89

Variable Selection for Support Vector Machines in Moderately High Dimensions

Zhang, X., Wu, Y., Wang, L., & Li, R. (2015) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12100 · Cited by 89

Support vector machines with selective kernel scaling for protein classification and identification of key amino acid positions

Zavaljevski, N., Stevens, F. J., & Jaques Reifman, A. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.5.689 · Cited by 89

Cancer Classification from Gene Expression Data by NPPC Ensemble

Ghorai, S., Mukherjee, A., Sengupta, S., & Dutta, P. K. (2010) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2010.36 · Cited by 89

MAAR: Robust features to detect malicious activity based on API calls, their arguments and return values

Salehi, Z., Sami, A., & Ghiasi, M. (2016) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2016.12.016 · Cited by 89

Dynamic graph learning for spectral feature selection

Zheng, W., Zhu, X., Zhu, Y., Hu, R., & Lei, C. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-5272-y · Cited by 88

Evaluation of machine learning algorithms performance for the prediction of early multiple sclerosis from resting-state FMRI connectivity data

Saccà, V., Sarica, A., Novellino, F., Barone, S., Tallarico, T., Filippelli, E., Granata, A., Chiriaco, C., Bossio, R. B., Valentino, P., et al. (2018) · Brain Imaging and Behavior · doi:10.1007/s11682-018-9926-9 · Cited by 88

Overall survival prediction in glioblastoma multiforme patients from volumetric, shape and texture features using machine learning

Sanghani, P., Ang, B. T., King, N. K. K., & Ren, H. (2018) · Surgical Oncology · doi:10.1016/j.suronc.2018.09.002 · Cited by 88

Severe Dengue Prognosis Using Human Genome Data and Machine Learning

Davi, C. C. M., Pastor, A. F., Oliveira, T., Neto, F. B. D. L., Braga-Neto, U., Bigham, A. W., Bamshad, M. J., Marques, E. T. A., & Acioli‐Santos, B. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2897285 · Cited by 88

Automatic discovery of 100-miRNA signature for cancer classification using ensemble feature selection

Lopez‐Rincon, A., Martínez‐Archundia, M., Martinez‐Ruíz, G. U., Schönhuth, A., & Tonda, A. P. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3050-8 · Cited by 88

Assessing strategic flight schedules at an airport using machine learning-based flight delay and cancellation predictions

Lambelho, M., Mitici, M., Pickup, S., & Marsden, A. (2019) · Journal of Air Transport Management · doi:10.1016/j.jairtraman.2019.101737 · Cited by 88

An Efficient hybrid filter-wrapper metaheuristic-based gene selection method for high dimensional datasets

Pirgazi, J., Alimoradi, M., Abharian, T. E., & Olyaee, M. H. (2019) · Scientific Reports · doi:10.1038/s41598-019-54987-1 · Cited by 88

Predicting lane-changing risk level based on vehicles’ space-series features: A pre-emptive learning approach

Chen, T., Shi, X., Wong, Y. D., & Yu, X. (2020) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2020.102646 · Cited by 88

A robust SVM-based approach with feature selection and outliers detection for classification problems

Baldomero‐Naranjo, M., Martínez-Merino, L. I., & Rodríguez-Chía, A. M. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115017 · Cited by 88

A Hybrid Barnacles Mating Optimizer Algorithm With Support Vector Machines for Gene Selection of Microarray Cancer Classification

Houssein, E. H., AbdElminaam, D. S., Hassan, H. N., Al-Sayed, M. M., & Nabil, E. (2021) · IEEE Access · doi:10.1109/access.2021.3075942 · Cited by 88

Improving the Performance of SVM-RFE to Select Genes in Microarray Data

Ding, Y., & Wilkins, D. E. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-s2-s12 · Cited by 88

Drug/nondrug classification using Support Vector Machines with various feature selection strategies

Korkmaz, S., Zararsız, G., & Göksülük, D. (2014) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2014.08.009 · Cited by 88

Online chatter detection of the end milling based on wavelet packet transform and support vector machine recursive feature elimination

Chen, G. S., & Zheng, Q. (2017) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-017-1242-9 · Cited by 87

Baseline exposure, antibody subclass, and hepatitis B response differentially affect malaria protective immunity following RTS,S/AS01E vaccination in African children

Ubillos, I., Ayestaran, A., Nhabomba, A. J., Dosoo, D., Vidal, M., Jiménez, A., Jairoce, C., Sanz, H., Aguilar, R., Williams, N. A., et al. (2018) · BMC Medicine · doi:10.1186/s12916-018-1186-4 · Cited by 87

Combining Multiple Feature-Ranking Techniques and Clustering of Variables for Feature Selection

Haq, A. U., Zhang, D., Peng, H., & Rahman, S. U. (2019) · IEEE Access · doi:10.1109/access.2019.2947701 · Cited by 87

Applying Machine Learning to Kinematic and Eye Movement Features of a Movement Imitation Task to Predict Autism Diagnosis

Vabalas, A., Gowen, E., Poliakoff, E., & Casson, A. J. (2020) · Scientific Reports · doi:10.1038/s41598-020-65384-4 · Cited by 87

A Survey on Feature Selection Techniques Based on Filtering Methods for Cyber Attack Detection

Lyu, Y., Feng, Y., & Sakurai, K. (2023) · Information · doi:10.3390/info14030191 · Cited by 87

Online Feature Selection with Group Structure Analysis

Wang, J., Wang, M., Li, P., Liu, L., Zhao, Z., Hu, X., & Wu, X. (2015) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2015.2441716 · Cited by 87

Comparison of a healthy miRNome with melanoma patient miRNomes: are microRNAs suitable serum biomarkers for cancer?

Margue, C., Reinsbach, S. E., Philippidou, D., Beaume, N., Walters, C., Schneider, J. G., Nashan, D., Behrmann, I., & Kreis, S. (2015) · Oncotarget · doi:10.18632/oncotarget.3661 · Cited by 87

A comparison of different chemometrics approaches for the robust classification of electronic nose data

Gromski, P. S., Correa, E. S., Vaughan, A. A., Wedge, D. C., Turner, M. L., & Goodacre, R. (2014) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-014-8216-7 · Cited by 87

A Variance Minimization Criterion to Feature Selection Using Laplacian Regularization

He, X., Ji, M., Zhang, C., & Bao, H. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.44 · Cited by 87

Classifier ensembles for fMRI data analysis: an experiment

Kuncheva, L. I., & Rodríguez, J. J. (2010) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2009.12.021 · Cited by 87

Tumor classification by combining PNN classifier ensemble with neighborhood rough set based gene reduction

Wang, S., Li, X., Zhang, S., Gui, J., & Huang, D.-S. (2009) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2009.11.014 · Cited by 87

Identifying cancer biomarkers by network-constrained support vector machines

Chen, L., Xuan, J., Riggins, R. B., Clarke, R. C., & Wang, Y. (2011) · BMC Systems Biology · doi:10.1186/1752-0509-5-161 · Cited by 87

Identifying Predictive Features in Drug Response Using Machine Learning: Opportunities and Challenges

Vidyasagar, M. (2014) · The Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010814-124502 · Cited by 87

Rapid Mass Spectrometric Metabolic Profiling of Blood Sera Detects Ovarian Cancer with High Accuracy

Zhou, M., Guan, W., Walker, L. D., Mezencev, R., Benigno, B. B., Gray, A., Fernández, F. M., & McDonald, J. F. (2010) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-10-0126 · Cited by 87

A reference model for customer-centric data mining with support vector machines

Lessmann, S., & Voß, S. (2009) · European Journal of Operational Research · doi:10.1016/j.ejor.2008.12.017 · Cited by 86

Feature Selection Using a Neural Framework With Controlled Redundancy

Chakraborty, R., & Pal, N. R. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2308902 · Cited by 86

An ensemble correlation-based gene selection algorithm for cancer classification with gene expression data

Piao, Y., Piao, M., Park, K., & Ryu, K. H. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts602 · Cited by 86

Bioacoustic detection with wavelet-conditioned convolutional neural networks

Kiskin, I., Zilli, D., Li, Y., Sinka, M. E., Willis, K. J., & Roberts, S. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3626-7 · Cited by 86

Deep-FS: A feature selection algorithm for Deep Boltzmann Machines

Taherkhani, A., Cosma, G., & McGinnity, T. M. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.09.040 · Cited by 86

Mutual information based multi-label feature selection via constrained convex optimization

Sun, Z., Zhang, J., Dai, L., Li, C., Zhou, C., Xin, J., & Li, S. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.10.047 · Cited by 86

C-HMOSHSSA: Gene selection for cancer classification using multi-objective meta-heuristic and machine learning methods

Sharma, A., & Rani, R. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.06.029 · Cited by 86

Automated detection of cognitive engagement to inform the art of staying engaged in problem-solving

Li, S., Lajoie, S. P., Zheng, J., Wu, H., & Cheng, H. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.104114 · Cited by 86

Feature selection on quantum computers

Mücke, S., Heese, R., Müller, S., Wolter, M., & Piatkowski, N. (2023) · Quantum Machine Intelligence · doi:10.1007/s42484-023-00099-z · Cited by 86

Similarity of feature selection methods: An empirical study across data intensive classification tasks

Dessì, N., & Pes, B. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.01.069 · Cited by 86

An efficient gene selection algorithm based on mutual information

Cai, R., Hao, Z., Yang, X., & Wen, W. (2008) · Neurocomputing · doi:10.1016/j.neucom.2008.04.005 · Cited by 86

Gene extraction for cancer diagnosis by support vector machines—An improvement

Huang, T. M., & Kecman, V. (2005) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2005.01.006 · Cited by 86

Feature Selection as a One-Player Game

Gaudel, R., & Sébag, M. (2010) · Cited by 86

Classification of Future Electricity Market Prices

Zareipour, H., Janjani, A., Leung, H., Motamedi, A., & Schellenberg, A. (2010) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2010.2052116 · Cited by 86

Bio-Inspired Credit Risk Analysis

Yu, L., Wang, S., Lai, K. K., & Zhou, L. (2008) · Springer eBooks · doi:10.1007/978-3-540-77803-5 · Cited by 86

ADMET Evaluation in Drug Discovery. Part 17: Development of Quantitative and Qualitative Prediction Models for Chemical-Induced Respiratory Toxicity

Lei, T., Chen, F., Liu, H., Sun, H., Kang, Y., Li, D., Li, Y., & Hou, T. (2017) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.7b00317 · Cited by 86

Classification and feature selection algorithms for multi-class CGH data

Liu, J., Ranka, S., & Kahveci, T. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn145 · Cited by 85

Hierarchical Anatomical Brain Networks for MCI Prediction: Revisiting Volumetric Measures

Zhou, L., Wang, Y., Yang, L., Yap, P., Shen, D. G., & (ADNI), A. T. A. D. N. I. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021935 · Cited by 85

A New Hybrid Intelligent Framework for Predicting Parkinson’s Disease

Cai, Z., Gu, J., & Chen, H. (2017) · IEEE Access · doi:10.1109/access.2017.2741521 · Cited by 85

Automatic detection of major depressive disorder using electrodermal activity

Kim, A. Y., Jang, E. H., Kim, S., Choi, K. W., Jeon, H. J., Yu, H. Y., & Byun, S. (2018) · Scientific Reports · doi:10.1038/s41598-018-35147-3 · Cited by 85

A comparison and assessment of computational method for identifying recombination hotspots inSaccharomyces cerevisiae

Yang, H. Q., Yang, W., Dao, F., Lv, H., Ding, H., Chen, W., & Lin, H. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz123 · Cited by 85

sigFeature: Novel Significant Feature Selection Method for Classification of Gene Expression Data Using Support Vector Machine and t Statistic

Das, P. K., Roychowdhury, A., Das, S., Roychoudhury, S., & Tripathy, S. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00247 · Cited by 85

Production prediction at ultra-high water cut stage via Recurrent Neural Network

Wang, H., Mu, L., Fugeng, S., & Dou, H. (2020) · Petroleum Exploration and Development · doi:10.1016/s1876-3804(20)60119-7 · Cited by 85

Development of a hybrid proximal sensing method for rapid identification of petroleum contaminated soils

Chakraborty, S., Weindorf, D. C., Ли, Б., Aldabaa, A. A. A., Ghosh, R. K., Paul, S., & Ali, M. N. (2015) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2015.01.087 · Cited by 85

Using cooperative game theory to optimize the feature selection problem

Sun, X., Liu, Y., Li, J., Zhu, J., Liu, X., & Chen, H. (2012) · Neurocomputing · doi:10.1016/j.neucom.2012.05.001 · Cited by 85

Remote Sensing Feature Selection by Kernel Dependence Measures

Camps‐Valls, G., Mooij, J. M., & Schölkopf, B. (2010) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2010.2041896 · Cited by 85

LS Bound based gene selection for DNA microarray data

Zhou, X., & Mao, K. (2004) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti216 · Cited by 85

Development of a two-stage gene selection method that incorporates a novel hybrid approach using the cuckoo optimization algorithm and harmony search for cancer classification

Elyasigomari, V., Lee, D. A., Screen, H. R. C., & Shaheed, M. H. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.01.016 · Cited by 84

A Comparative Study of Feature Selection Methods for Stress Hotspot Classification in Materials

Mangal, A., & Holm, E. A. (2018) · Integrating materials and manufacturing innovation · doi:10.1007/s40192-018-0109-8 · Cited by 84

Evaluation of global climate models for precipitation projection in sub-Himalaya region of Pakistan

Iqbal, Z., Shahid, S., Ahmed, K., Ismail, T., Khan, N., Virk, Z. T., & Johar, W. (2020) · Atmospheric Research · doi:10.1016/j.atmosres.2020.105061 · Cited by 84

SVR-FFS: A novel forward feature selection approach for high-frequency time series forecasting using support vector regression

Valente, J. M., & Maldonado, S. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113729 · Cited by 84

Groundwater Salinity Susceptibility Mapping Using Classifier Ensemble and Bayesian Machine Learning Models

Mosavi, A., Hosseini, F. S., Choubin, B., Goodarzi, M., & Dineva, A. A. (2020) · IEEE Access · doi:10.1109/access.2020.3014908 · Cited by 84

Crude oil price forecasting incorporating news text

Bai, Y. F., Li, X., Hao, Y., & Jia, S. (2021) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2021.06.006 · Cited by 84

MAGE: Automatic diagnosis of autism spectrum disorders using multi-atlas graph convolutional networks and ensemble learning

Wang, Y., Liu, J., Xiang, Y., Wang, J., Chen, Q., & Chong, J. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.06.152 · Cited by 84

Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield and Amygdala Volume of Structural MRI

Khatri, U., & Kwon, G. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.818871 · Cited by 84

Interpretable Machine Learning for Discovery: Statistical Challenges and Opportunities

Allen, G. I., Gan, L., & Zheng, L. (2023) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-040120-030919 · Cited by 84

Predicting radiotherapy outcomes using statistical learning techniques

Naqa, I. E., Bradley, J. D., Lindsay, P. E., Hope, A. J., & Deasy, J. O. (2009) · Physics in Medicine and Biology · doi:10.1088/0031-9155/54/18/s02 · Cited by 84

Ultrasound RF Time Series for Classification of Breast Lesions

Uniyal, N., Eskandari, H., Abolmaesumi, P., Sojoudi, S., Gordon, P. B., Warren, L. J., Rohling, R. N., Salcudean, S. E., & Moradi, M. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2365030 · Cited by 84

penalizedSVM: a R-package for feature selection SVM classification

Becker, N., Werft, W., Toedt, G., Lichter, P., & Benner, A. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp286 · Cited by 84

Three-dimensional texture features from intensity and high-order derivative maps for the discrimination between bladder tumors and wall tissues via MRI

Xu, X., Zhang, X., Tian, Q., Zhang, G., Liu, Y., Cui, G., Meng, J., Wu, Y., Liu, T., Yang, Z., et al. (2017) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-017-1522-8 · Cited by 84

High-Resolution Genomic Profiling Reveals Association of Chromosomal Aberrations on 1q and 16p with Histologic and Genetic Subgroups of Invasive Breast Cancer

Stange, D. E., Radlwimmer, B., Schubert, F., Traub, F., Pich, A., Toedt, G., Mendrzyk, F., Lehmann, U., Eils, R., Kreipe, H. H., et al. (2006) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-05-1633 · Cited by 84

Big-Data Analysis, Cluster Analysis, and Machine-Learning Approaches

Alonso‐Betanzos, A., & Bolón‐Canedo, V. (2018) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-77932-4_37 · Cited by 83

Machine Learning Based Real-Time Monitoring of Long-Term Voltage Stability Using Voltage Stability Indices

Dharmapala, K., Rajapakse, A. D., Narendra, K., & Zhang, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3043935 · Cited by 83

The Phishing Funnel Model: A Design Artifact to Predict User Susceptibility to Phishing Websites

Abbasi, A., Dobolyi, D. G., Vance, A., & Zahedi, F. M. (2021) · Information Systems Research · doi:10.1287/isre.2020.0973 · Cited by 83

Unsupervised feature selection via adaptive autoencoder with redundancy control

Gong, X., Yu, L. M., Wang, J., Zhang, K., Bai, X. P., & Pal, N. R. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.03.004 · Cited by 83

Improving pedotransfer functions for predicting soil mineral associated organic carbon by ensemble machine learning

Xiao, Y., Xue, J., Zhang, X., Wang, N., Hong, Y., Jiang, Y., Zhou, Y., Teng, H., Hu, B., Lugato, E., et al. (2022) · Geoderma · doi:10.1016/j.geoderma.2022.116208 · Cited by 83

Wavelength and texture feature selection for hyperspectral imaging: a systematic literature review

Rogers, M., Blanc-Talon, J., Urschler, M., & Delmas, P. J. (2023) · Journal of Food Measurement & Characterization · doi:10.1007/s11694-023-02044-x · Cited by 83

Stacking Ensemble Deep Learning for Real-Time Intrusion Detection in IoMT Environments

Alalwany, E., Alsharif, B., Alotaibi, Y., Alfahaid, A., Mahgoub, I., & Ilyas, M. (2025) · Sensors · doi:10.3390/s25030624 · Cited by 83

A Study On Feature Selection And Classification Techniques For Automatic Genre Classification Of Traditional Malay Music.

Doraisamy, S. C., Golzari, S., Norowi, N. M., Sulaiman, M. N., & Udzir, N. I. (2008) · Universiti Putra Malaysia Institutional Repository (Universiti Putra Malaysia) · doi:10.5281/zenodo.1415123 · Cited by 83

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