An introduction to variable and feature selection

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

Machine Learning in Medicine

Deo, R. C. (2015) · Circulation · doi:10.1161/circulationaha.115.001593 · Cited by 3,466

A survey of robot learning from demonstration

Argall, B. D., Chernova, S., Veloso, M., & Browning, B. (2009) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2008.10.024 · Cited by 3,227

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

Data-driven Soft Sensors in the process industry

Kadlec, P., Gabrys, B., & Strandt, S. (2009) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2008.12.012 · Cited by 1,845

A tutorial on human activity recognition using body-worn inertial sensors

Bulling, A., Blanke, U., & Schiele, B. (2014) · ACM Computing Surveys · doi:10.1145/2499621 · Cited by 1,668

Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines

Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., & Chica-Rivas, M. (2015) · Ore Geology Reviews · doi:10.1016/j.oregeorev.2015.01.001 · Cited by 1,576

Machine learning and data mining methods in diabetes research

Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2016.12.005 · Cited by 1,445

Particle Swarm Optimization: A Comprehensive Survey

Shami, T. M., El-Saleh, A. A., Alswaitti, M., Al-Tashi, Q., Summakieh, M. A., & Mirjalili, S. (2022) · IEEE Access · doi:10.1109/access.2022.3142859 · Cited by 1,584

Relief-based feature selection: Introduction and review

Urbanowicz, R. J., Meeker, M., La Cava, W., Olson, R. S., & Moore, J. H. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.07.014 · Cited by 1,311

Materials discovery and design using machine learning

Liu, Y., Zhao, T., Ju, W., & Shi, S. (2017) · Journal of Materiomics · doi:10.1016/j.jmat.2017.08.002 · Cited by 1,276

Modeling wine preferences by data mining from physicochemical properties

Cortez, P., Cerdeira, A., Almeida, F., Matos, T., & Reis, J. (2009) · Decision Support Systems · doi:10.1016/j.dss.2009.05.016 · Cited by 1,273

Evaluating the Visualization of What a Deep Neural Network Has Learned

Samek, W., Binder, A., Montavon, G., Lapuschkin, S., & Muller, K.-R. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2599820 · Cited by 1,164

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

Soli: ubiquitous gesture sensing with millimeter wave radar

Lien, J., Gillian, N., Karagozler, M. E., Amihood, P., Schwesig, C., Olson, E., Raja, H., & Poupyrev, I. (2016) · ACM Transactions on Graphics · doi:10.1145/2897824.2925953 · Cited by 957

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

A survey on deep learning based bearing fault diagnosis

Hoang, D.-T., & Kang, H.-J. (2019) · Neurocomputing · doi:10.1016/j.neucom.2018.06.078 · Cited by 866

Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis

Zeng, L.-L., Shen, H., Liu, L., Wang, L., Li, B., Fang, P., Zhou, Z., Li, Y., & Hu, D. (2012) · Brain · doi:10.1093/brain/aws059 · Cited by 748

Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems

Datta, A., Sen, S., & Zick, Y. (2016) · 2016 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2016.42 · Cited by 728

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

Ensembles for feature selection: A review and future trends

Bolón-Canedo, V., & Alonso-Betanzos, A. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.11.008 · Cited by 541

Accurate intelligible models with pairwise interactions

Lou, Y., Caruana, R., Gehrke, J., & Hooker, G. (2013) · Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2487575.2487579 · Cited by 564

MODIS detected surface urban heat islands and sinks: Global locations and controls

Clinton, N., & Gong, P. (2013) · Remote Sensing of Environment · doi:10.1016/j.rse.2013.03.008 · Cited by 521

Applications of machine learning in animal behaviour studies

Valletta, J. J., Torney, C., Kings, M., Thornton, A., & Madden, J. (2017) · Animal Behaviour · doi:10.1016/j.anbehav.2016.12.005 · Cited by 505

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

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

Fame for sale: Efficient detection of fake Twitter followers

Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2015) · Decision Support Systems · doi:10.1016/j.dss.2015.09.003 · Cited by 475

Emotion recognition using wireless signals

Zhao, M., Adib, F., & Katabi, D. (2016) · Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking · doi:10.1145/2973750.2973762 · Cited by 461

Rolling element bearing fault diagnosis using convolutional neural network and vibration image

Hoang, D.-T., & Kang, H.-J. (2019) · Cognitive Systems Research · doi:10.1016/j.cogsys.2018.03.002 · Cited by 450

MIFS-ND: A mutual information-based feature selection method

Hoque, N., Bhattacharyya, D. K., & Kalita, J. K. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.04.019 · Cited by 442

An up-to-date comparison of state-of-the-art classification algorithms

Zhang, C., Liu, C., Zhang, X., & Almpanidis, G. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.04.003 · Cited by 440

A survey on wearable sensor modality centred human activity recognition in health care

Wang, Y., Cang, S., & Yu, H. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.04.057 · Cited by 409

Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study

Liang, D., Lu, C.-C., Tsai, C.-F., & Shih, G.-A. (2016) · European Journal of Operational Research · doi:10.1016/j.ejor.2016.01.012 · Cited by 405

High-dimensional data clustering

Bouveyron, C., Girard, S., & Schmid, C. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2007.02.009 · Cited by 382

Machine learning and integrative analysis of biomedical big data

Mirza, B., Wang, W., Wang, J., Choi, H., Chung, N. C., & Ping, P. (2019) · Genes · doi:10.3390/genes10020087 · Cited by 380

Applications of Artificial Intelligence in Machine Learning: Review and Prospect

Das, S., Dey, A., Pal, A., & Roy, N. (2015) · International Journal of Computer Applications · doi:10.5120/20182-2402 · Cited by 360

A Multiplexed, Heterogeneous, and Adaptive Code for Navigation in Medial Entorhinal Cortex

Hardcastle, K., Maheswaranathan, N., Ganguli, S., & Giocomo, L. M. (2017) · Neuron · doi:10.1016/j.neuron.2017.03.025 · Cited by 312

Distinct Early Serological Signatures Track with SARS-CoV-2 Survival

Atyeo, C., Fischinger, S., Zohar, T., Slein, M. D., Burke, J., Loos, C., McCulloch, D. J., Newman, K. L., Wolf, C., Yu, J., et al. (2020) · Immunity · doi:10.1016/j.immuni.2020.07.020 · Cited by 349

Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records

Thorsen-Meyer, H.-C., Nielsen, A. B., Nielsen, A. P., Kaas-Hansen, B. S., Toft, P., Schierbeck, J., Strøm, T., Chmura, P. J., Heimann, M., Dybdahl, L., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30018-2 · Cited by 336

Text-Based Twitter User Geolocation Prediction

Han, B., Cook, P., & Baldwin, T. (2014) · Journal of Artificial Intelligence Research · doi:10.1613/jair.4200 · Cited by 324

Predicting hospital admission at emergency department triage using machine learning

Hong, W. S., Haimovich, A. D., & Taylor, R. A. (2018) · PLOS ONE · doi:10.1371/journal.pone.0201016 · Cited by 326

Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images

Chu, C., Hsu, A.-L., Chou, K.-H., Bandettini, P., & Lin, C. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.11.066 · Cited by 319

Tutorial on practical tips of the most influential data preprocessing algorithms in data mining

García, S., Luengo, J., & Herrera, F. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.12.006 · Cited by 310

Clinical applications of the functional connectome

Castellanos, F. X., Di Martino, A., Craddock, R. C., Mehta, A. D., & Milham, M. P. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.083 · Cited by 312

Feature selection with multi-view data: A survey

Zhang, R., Nie, F., Li, X., & Wei, X. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.11.019 · Cited by 313

The Shapley Value in Machine Learning

Rozemberczki, B., Watson, L., Bayer, P., Yang, H.-T., Kiss, O., Nilsson, S., & Sarkar, R. (2022) · arXiv · doi:10.48550/arxiv.2202.05594 · Cited by 312

Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships

Janet, J. P., & Kulik, H. J. (2017) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.7b08750 · Cited by 289

Offline/realtime traffic classification using semi-supervised learning

Erman, J., Mahanti, A., Arlitt, M., Cohen, I., & Williamson, C. (2007) · Performance Evaluation · doi:10.1016/j.peva.2007.06.014 · Cited by 287

Diagnostic neuroimaging across diseases

Klöppel, S., Abdulkadir, A., Jack, C. R., Koutsouleris, N., Mourão-Miranda, J., & Vemuri, P. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.11.002 · Cited by 279

Automatic identification of artifacts in electrodermal activity data

Taylor, S., Jaques, N., Chen, W., Fedor, S., Sano, A., & Picard, R. (2015) · 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7318762 · Cited by 279

Approximate model predictive building control via machine learning

Drgoňa, J., Picard, D., Kvasnica, M., & Helsen, L. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.02.156 · Cited by 260

Structural Health Monitoring in Composite Structures: A Comprehensive Review

Hassani, S., Mousavi, M., & Gandomi, A. H. (2021) · Sensors · doi:10.3390/s22010153 · Cited by 258

Learning and comparing functional connectomes across subjects

Varoquaux, G., & Craddock, R. C. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.007 · Cited by 233

Prognostic and diagnostic potential of the structural neuroanatomy of depression

Costafreda, S. G., Chu, C., Ashburner, J., & Fu, C. H. Y. (2009) · PLoS ONE · doi:10.1371/journal.pone.0006353 · Cited by 232

Frontal alpha oscillations distinguish leaders from followers: Multivariate decoding of mutually interacting brains

Konvalinka, I., Bauer, M., Stahlhut, C., Hansen, L. K., Roepstorff, A., & Frith, C. D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.03.003 · Cited by 228

An empirical analysis of data preprocessing for machine learning-based software cost estimation

Huang, J., Li, Y.-F., & Xie, M. (2015) · Information and Software Technology · doi:10.1016/j.infsof.2015.07.004 · Cited by 223

Data preprocessing in predictive data mining

Alexandropoulos, S.-A. N., Kotsiantis, S. B., & Vrahatis, M. N. (2019) · The Knowledge Engineering Review · doi:10.1017/s026988891800036x · Cited by 209

Coregulation of Transcription Factor Binding and Nucleosome Occupancy through DNA Features of Mammalian Enhancers

Barozzi, I., Simonatto, M., Bonifacio, S., Yang, L., Rohs, R., Ghisletti, S., & Natoli, G. (2014) · Molecular Cell · doi:10.1016/j.molcel.2014.04.006 · Cited by 209

Decoding the perception of pain from fMRI using multivariate pattern analysis

Brodersen, K. H., Wiech, K., Lomakina, E. I., Lin, C.-S., Buhmann, J. M., Bingel, U., Ploner, M., Stephan, K. E., & Tracey, I. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.08.035 · Cited by 200

Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease

Vivekanandan, T., & Sriman Narayana Iyengar, N. C. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.09.011 · Cited by 185

Application of data fusion techniques and technologies for wearable health monitoring

King, R. C., Villeneuve, E., White, R. J., Sherratt, R. S., Holderbaum, W., & Harwin, W. S. (2017) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2016.12.011 · Cited by 182

A Combined Model- and Learning-Based Framework for Interaction-Aware Maneuver Prediction

Bahram, M., Hubmann, C., Lawitzky, A., Aeberhard, M., & Wollherr, D. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2506642 · Cited by 175

Differential evolution trained wavelet neural networks: Application to bankruptcy prediction in banks

Chauhan, N., Ravi, V., & Karthik Chandra, D. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.09.019 · Cited by 180

Benchmark of filter methods for feature selection in high-dimensional gene expression survival data

Bommert, A., Welchowski, T., Schmid, M., & Rahnenführer, J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab354 · Cited by 182

Rumor Identification in Microblogging Systems Based on Users’ Behavior

Liang, G., He, W., Xu, C., Chen, L., & Zeng, J. (2015) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2016.2517458 · Cited by 169

A Reinforcement Learning Framework for Explainable Recommendation

Wang, X., Chen, Y., Yang, J., Wu, L., Wu, Z., & Xie, X. (2018) · 2018 IEEE International Conference on Data Mining (ICDM) · doi:10.1109/icdm.2018.00074 · Cited by 163

Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods

Suryanarayana, G., Lago, J., Geysen, D., Aleksiejuk, P., & Johansson, C. (2018) · Energy · doi:10.1016/j.energy.2018.05.111 · Cited by 155

Binary biogeography-based optimization based SVM-RFE for feature selection

Albashish, D., Hammouri, A. I., Braik, M., Atwan, J., & Sahran, S. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2020.107026 · Cited by 154

Automated trading with performance weighted random forests and seasonality

Booth, A., Gerding, E., & McGroarty, F. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.12.009 · Cited by 154

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

A Comprehensive Study of Activity Recognition Using Accelerometers

Twomey, N., Diethe, T., Fafoutis, X., Elsts, A., McConville, R., Flach, P., & Craddock, I. (2018) · Informatics · doi:10.3390/informatics5020027 · Cited by 143

Insider Threat Detection Based on User Behavior Modeling and Anomaly Detection Algorithms

Kim, J., Park, M., Kim, H., Cho, S., & Kang, P. (2019) · Applied Sciences · doi:10.3390/app9194018 · Cited by 134

Visual pattern mining in histology image collections using bag of features

Cruz-Roa, A., Caicedo, J. C., & González, F. A. (2011) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2011.04.010 · Cited by 133

Promises, Pitfalls, and Basic Guidelines for Applying Machine Learning Classifiers to Psychiatric Imaging Data, with Autism as an Example

Kassraian-Fard, P., Matthis, C., Balsters, J. H., Maathuis, M. H., & Wenderoth, N. (2016) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2016.00177 · Cited by 132

Clinical text classification research trends: Systematic literature review and open issues

Mujtaba, G., Shuib, L., Idris, N., Hoo, W. L., Raj, R. G., Khowaja, K., Shaikh, K., & Nweke, H. F. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.09.034 · Cited by 123

Overfitting in linear feature extraction for classification of high-dimensional image data

Liu, R., & Gillies, D. F. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2015.11.015 · Cited by 123

Bayesian networks for interpretable machine learning and optimization

Mihaljević, B., Bielza, C., & Larrañaga, P. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.01.138 · Cited by 123

Bias and Class Imbalance in Oncologic Data—Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets

Tasci, E., Zhuge, Y., Camphausen, K., & Krauze, A. V. (2022) · Cancers · doi:10.3390/cancers14122897 · Cited by 117

Improved marketing decision making in a customer churn prediction context using generalized additive models

Coussement, K., Benoit, D. F., & Van den Poel, D. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.07.029 · Cited by 117

Towards a Lightweight Detection System for Cyber Attacks in the IoT Environment Using Corresponding Features

Soe, Y. N., Feng, Y., Santosa, P. I., Hartanto, R., & Sakurai, K. (2020) · Electronics · doi:10.3390/electronics9010144 · Cited by 110

Machine Learning-Enabled Internet of Things (IoT): Data, Applications, and Industry Perspective

Bzai, J., Alam, F., Dhafer, A., Bojović, M., Altowaijri, S. M., Niazi, I. K., & Mehmood, R. (2022) · Electronics · doi:10.3390/electronics11172676 · Cited by 110

Land-surface parameters for spatial predictive mapping and modeling

Maxwell, A. E., & Shobe, C. M. (2022) · Earth-Science Reviews · doi:10.1016/j.earscirev.2022.103944 · Cited by 105

Evaluating the impact of multivariate imputation by MICE in feature selection

Mera-Gaona, M., Neumann, U., Vargas-Canas, R., & López, D. M. (2021) · PLOS ONE · doi:10.1371/journal.pone.0254720 · Cited by 96

COM: A method for mining and monitoring human activity patterns in home-based health monitoring systems

Rashidi, P., & Cook, D. J. (2013) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2508037.2508045 · Cited by 94

Statistical analysis of proteomics data: A review on feature selection

Lualdi, M., & Fasano, M. (2019) · Journal of Proteomics · doi:10.1016/j.jprot.2018.12.004 · Cited by 93

Tree-based ensemble methods for sensitivity analysis of environmental models: A performance comparison with Sobol and Morris techniques

Jaxa-Rozen, M., & Kwakkel, J. (2018) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2018.06.011 · Cited by 91

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

Conversion and time-to-conversion predictions of mild cognitive impairment using low-rank affinity pursuit denoising and matrix completion

Thung, K.-H., Yap, P.-T., Adeli, E., Lee, S.-W., & Shen, D. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.01.002 · Cited by 89

Influence of contextual information in emotion annotation for spoken dialogue systems

Callejas, Z., & López-Cózar, R. (2008) · Speech Communication · doi:10.1016/j.specom.2008.01.001 · Cited by 86

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

Say Anything: Using Textual Case-Based Reasoning to Enable Open-Domain Interactive Storytelling

Swanson, R., & Gordon, A. S. (2012) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/2362394.2362398 · Cited by 85

Interpretable Anomaly Prediction: Predicting anomalous behavior in industry 4.0 settings via regularized logistic regression tools

Langone, R., Cuzzocrea, A., & Skantzos, N. (2020) · Data & Knowledge Engineering · doi:10.1016/j.datak.2020.101850 · Cited by 81

Deep learning-based identification of genetic variants: application to Alzheimer’s disease classification

Jo, T., Nho, K., Bice, P., Saykin, A. J., & Initiative, F. T. A. D. N. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac022 · Cited by 80

Machine learning and individual variability in electric field characteristics predict tDCS treatment response

Albizu, A., Fang, R., Indahlastari, A., O’Shea, A., Stolte, S. E., See, K. B., Boutzoukas, E. M., Kraft, J. N., Nissim, N. R., & Woods, A. J. (2020) · Brain Stimulation · doi:10.1016/j.brs.2020.10.001 · Cited by 79

Analyzing Raman spectroscopic data

Ryabchykov, O., Guo, S., & Bocklitz, T. (2019) · Physical Sciences Reviews · doi:10.1515/psr-2017-0043 · Cited by 74

Multivariate pattern analysis strategies in detection of remitted major depressive disorder using resting state functional connectivity

Bhaumik, R., Jenkins, L. M., Gowins, J. R., Jacobs, R. H., Barba, A., Bhaumik, D. K., & Langenecker, S. A. (2017) · NeuroImage: Clinical · doi:10.1016/j.nicl.2016.02.018 · Cited by 72

No-Reference Video Quality Estimation Based on Machine Learning for Passive Gaming Video Streaming Applications

Barman, N., Jammeh, E., Ghorashi, S. A., & Martini, M. G. (2019) · IEEE Access · doi:10.1109/access.2019.2920477 · Cited by 71

Machine Learning in Human Olfactory Research

Lötsch, J., Kringel, D., & Hummel, T. (2019) · Chemical Senses · doi:10.1093/chemse/bjy067 · Cited by 71

Network, Anatomical, and Non-Imaging Measures for the Prediction of ADHD Diagnosis in Individual Subjects

Bohland, J. W., Saperstein, S., Pereira, F., Rapin, J., & Grady, L. (2012) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2012.00078 · Cited by 64