Classification with ensembles and case study on functional magnetic resonance imaging

Sagi, 2018, Ensemble learning: a survey, Wiley Interdiscipl. Rev.: Data Min. Knowl. Discov., 8, e1249 Löfström, 2015 Masih, 2019, Application of ensemble learning techniques to model the atmospheric concentration of so2, Glob. J. Environ. Sci. Manag., 5, 309 Bhowmick, 2019, Hecmi: hybrid ensemble technique for classification of multiclass imbalanced data, 109 Wang, 2019, Ensemble based fuzzy weighted extreme learning machine for gene expression classification, Appl. Intell., 49, 1161, 10.1007/s10489-018-1322-z Agrawal, 2012, Lung cancer survival prediction using ensemble data mining on seer data, Sci. Program., 20, 29 A. Mi, L. Wang, J. Qi, A Multiple Classifier Fusion Algorithm Using Weighted Decision Templates, Scientific Programming, (PT.2)(2016):1–10. N. Pérez-Díaz, D. Ruano-Ordás, F. Fdez-Riverola, J.R. Méndez, Boosting Accuracy of Classical Machine Learning Antispam Classifiers in Real Scenarios by Applying Rough Set Theory, Scientific Programming, https://doi.org/10.1155/2016/5945192. Zhang, 2019, A distance-based weighting framework for boosting the performance of dynamic ensemble selection, Inf. Process. Manag., 56, 1300, 10.1016/j.ipm.2019.03.009 Mohan, 2019, A novel bagging ensemble approach for predicting summertime ground-level ozone concentration, J. Air Waste Manag. Assoc., 69, 220, 10.1080/10962247.2018.1534701 Latouche, 1999, Introduction to Matrix Analytic Methods in Stochastic Modeling, J. Appl. Math. Stoc. Anal., 12, 435, 10.1155/S1048953399000362 Sharma, 2019, Extreme gradient boosting with squared logistic loss function, 313 Freund, 1999, A short introduction to boosting, J. Jpn. Soc. Artif. Intell., 14, 1612 Gosztolya, 2019, Calibrating adaboost for phoneme classification, Soft Comput., 23, 115, 10.1007/s00500-018-3577-z Freund, 1996, Experiments with a new boosting algorithm, Icml, 96, 148 Rokach, 2009, Taxonomy for characterizing ensemble methods in classification tasks: a review and annotated bibliography, Comput. Stat. Data Anal., 53, 4046, 10.1016/j.csda.2009.07.017 Kuncheva, 2004 Brown, 2005, Diversity creation methods: a survey and categorisation, Inf. Fusion, 6, 5, 10.1016/j.inffus.2004.04.004 Valentini, 2002, Ensembles of learning machines, 3 Kamel, 2003, Data dependence in combining classifiers, 1 Liu, 2008, Extreme support vector machine classifier, 222 Miche, 2010, Op-elm: optimally pruned extreme learning machine, IEEE Trans. Neural Network., 21, 158, 10.1109/TNN.2009.2036259 Huang, 2012, Extreme learning machine for regression and multiclass classification, IEEE Trans. Syst. Man Cybern. B (Cybern.), 42, 513, 10.1109/TSMCB.2011.2168604 Ravikumar, 2016, Image segmentation and classification of white blood cells with the extreme learning machine and the fast relevance vector machine, Artif. Cells Nanomed. Biotechnol., 44, 985 Atiquzzaman, 2016, Prediction of hydrological time-series using extreme learning machine, J. Hydroinf., 18, 345, 10.2166/hydro.2015.020 Deng, 2009, Regularized extreme learning machine, 389 Huang, 2010, Optimization method based extreme learning machine for classification, Neurocomputing, 74, 155, 10.1016/j.neucom.2010.02.019 Luo, 2015, A novel data fusion scheme using grey model and extreme learning machine in wireless sensor networks, Int. J. Contr. Autom. Syst., 13, 539, 10.1007/s12555-014-0309-8 Luo, 2016, Regression and classification using extreme learning machine based on l 1-norm and l 2-norm, Neurocomputing, 174, 179, 10.1016/j.neucom.2015.03.112 Wang, 2010, Dynamic adaboost ensemble extreme learning machine, vol. 3, V3 Liu, 2010, Ensemble based extreme learning machine, IEEE Signal Process. Lett., 17, 754, 10.1109/LSP.2010.2053356 Ayerdi, 2015, Spatially regularized semisupervised ensembles of extreme learning machines for hyperspectral image segmentation, Neurocomputing, 149, 373, 10.1016/j.neucom.2014.01.068 Wang, 2015, Extreme learning machine ensemble classifier for large-scale data, vol. 1, 151 Huang, 2015, Parallel ensemble of online sequential extreme learning machine based on mapreduce, vol. 1, 31 Cao, 2016, Landmark recognition with compact bow histogram and ensemble elm, Multimed. Tool. Appl., 75, 2839, 10.1007/s11042-014-2424-1 Jin, 2016, Ensemble based extreme learning machine for cross-modality face matching, Multimed. Tool. Appl., 75, 11831, 10.1007/s11042-015-2650-1 Wang, 2014, Parallelized extreme learning machine ensemble based on min–max modular network, Neurocomputing, 128, 31, 10.1016/j.neucom.2013.02.053 Zhang, 2016, Memetic extreme learning machine, Pattern Recogn., 58, 135, 10.1016/j.patcog.2016.04.003 Huang, 2004, Extreme learning machine: a new learning scheme of feed-forward neural networks, vol. 2, 985 Triguero, 2015, Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study, Knowl. Inf. Syst., 42, 245, 10.1007/s10115-013-0706-y Alcalá-Fdez, 2011, Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework, J. Mult.-Valued Log. Soft Comput, 1 Nene Bauer, 1999, An empirical comparison of voting classification algorithms: bagging, boosting, and variants, Mach. Learn., 36, 105, 10.1023/A:1007515423169 Lu, 2012, Tumor- classification using extreme learning machine ensemble, Math. Pract. Theory, 17, 148 Breiman, 1996, Bagging predictors, Mach. Learn., 24, 123, 10.1007/BF00058655 Li, 2014, Boosting weighted ELM for imbalanced learning, Neurocomputing, 128, 15, 10.1016/j.neucom.2013.05.051 Xing, 2013, Training extreme learning machine via regularized correntropy criterion, Neural Comput. Appl., 23, 1977, 10.1007/s00521-012-1184-y Shen, 2016, Kernel based semi-supervised extreme learning machine and the application in traffic congestion evaluation, vol. 1, 227 Huang, 2014, Semi-supervised and unsupervised extreme learning machines, IEEE Trans. Cybern., 44, 2405, 10.1109/TCYB.2014.2307349 University Mitchell, 2004, Learning to decode cognitive states from brain images, Mach. Learn., 57, 145, 10.1023/B:MACH.0000035475.85309.1b Friman, 2003 Wang, 2004, Training fmri classifiers to detect cognitive states across multiple human subjects, 709