Exploring multiobjective training in multiclass classification

H.A. Abbass, Pareto neuro-evolution: constructing ensemble, in: Congress on Evolutionary Computation, vol. 3, 2003, pp. 2074–2080. Acampora, 2018, A multi-objective evolutionary approach to training set selection for support vector machine, Knowledge-Based Systems, 147, 94, 10.1016/j.knosys.2018.02.022 Ahmadian, 2007, A new multi-objective evolutionary approach for creating ensemble of classifiers, 1031 Albukhanajer, 2017, Classifier ensembles for image identification using multi-objective Pareto features, Neurocomputing, 238, 316, 10.1016/j.neucom.2017.01.067 Bergstra, 2011, Algorithms for hyper-parameter optimization, 2546 Bergstra, 2012, Random search for hyper-parameter optimization, Journal of Machine Learning Research, 13, 281 U. Bhowan, M. Johnston, M. Zhang, Ensemble learning and pruning in multi-objective genetic programming for classification with unbalanced data, in: Advances in Artificial Intelligence, 2011, pp. 192–202. Bhowan, 2011, Evolving ensembles in Multi-objective Genetic Programming for classification with unbalanced data, 1331 Bhowan, 2013, Comparing ensemble learning approaches in genetic programming for classification with unbalanced data, 135 Breiman, 1996, Bagging predictors, Machine Learning, 24, 123, 10.1007/BF00058655 Breiman, 2001, Random forests, Machine Learning, 45, 5, 10.1023/A:1010933404324 Camilleri, 2014, An algorithmic approach to parameter selection in machine learning using meta-optimization techniques, WSEAS Transactions on Systems, 13, 203 Castillo, 2006, Multiobjective optimization of ensembles of multilayer perceptrons for pattern classification, 453 A. Chandra, H. Chen, X. Yao, Trade-off between diversity and accuracy in ensemble generation, in: Studies in Computational Intelligence, vol. 16, 2006, pp. 429–464. A. Chandra, X. Yao, DIVACE: Diverse and accurate ensemble learning algorithm, in: Intelligent Data Engineering and Automated Learning (IDEAL), 2004, pp. 619–625. Chandra, 2006, Ensemble learning using multi-objective evolutionary algorithms, Journal of Mathematical Modelling and Algorithms, 5, 417, 10.1007/s10852-005-9020-3 Chang, 2011, LIBSVM: A library for support vector machines, ACM Transactions on Intelligent Systems and Technology (TIST), 2, 1, 10.1145/1961189.1961199 Cohen, 1960, A coefficient of agreement for nominal scales, Educational and Psychological Measurement, 20, 37, 10.1177/001316446002000104 Cohon, 1978, vol. 140 Costa, 2003, Training neural networks with a multi-objective sliding mode control algorithm, Neurocomputing, 51, 467, 10.1016/S0925-2312(02)00697-5 Deb, 2002, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6, 182, 10.1109/4235.996017 Dos Santos, 2008, Pareto analysis for the selection of classifier ensembles, 681 Ekbal, 2010, Classifier ensemble using multiobjective optimization for named entity recognition, Frontiers in Artificial Intelligence and Applications, 215, 783 Ekbal, 2012, Multiobjective optimization for classifier ensemble and feature selection: an application to named entity recognition, International Journal on Document Analysis and Recognition, 15, 143, 10.1007/s10032-011-0155-7 Ekbal, 2016, Simultaneous feature and parameter selection using multiobjective optimization: application to named entity recognition, International Journal of Machine Learning and Cybernetics, 7, 597, 10.1007/s13042-014-0268-7 V. Engen, J. Vincent, A.C. Schierz, K. Phalp, Multi-objective evolution of the Pareto optimal set of neural network classifier ensembles, in: International Conference on Machine Learning and Cybernetics, vol. 1, 2009, pp. 74–79. Fernández Caballero, 2010, Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks, IEEE Transactions on Neural Networks, 21, 750, 10.1109/TNN.2010.2041468 Fernández-Delgado, 2014, Do we need hundreds of classifiers to solve real world classification problems?, Journal of Machine Learning Research, 15, 3133 Finner, 1993, On a monotonicity problem in step-down multiple test procedures, Journal of the American Statistical Association, 88, 920, 10.1080/01621459.1993.10476358 Friedman, 1937, The use of ranks to avoid the assumption of normality implicit in the analysis of variance, Journal of the American Statistical Association, 32, 675, 10.1080/01621459.1937.10503522 Hastie, 2009 Huang, 2012, Extreme learning machine for regression and multiclass classification, IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics, 42, 513, 10.1109/TSMCB.2011.2168604 C. Igel, Multi-objective model selection for support vector machines, in: Lecture Notes in Computer Science, vol. 3410, 2005, pp. 534–546. H. Ishibuchi, Y. Nojima, Difficulties in choosing a single final classifier from non-dominated solutions in multiobjective fuzzy genetics-based machine learning, in: Joint IFSA World Congress and NAFIPS Annual Meeting, 2013, pp. 1203–1208. Y. Jin, T. Okabe, B. Sendhoff, Neural network regularization and ensembling using multi-objective evolutionary algorithms, in: Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004, vol. 1, 2004, pp. 1–8. Jin, 2008, Pareto-based multiobjective machine learning: an overview and case studies, IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 38, 397, 10.1109/TSMCC.2008.919172 Jin, 2009, Pareto analysis of evolutionary and learning systems, Frontiers of Computer Science in China, 3, 4, 10.1007/s11704-009-0004-8 Jubril, 2012, A nonlinear weights selection in weighted sum for convex multiobjective optimization, Facta universitatis-series: Mathematics and Informatics, 27, 357 Kasimbeyli, 2019, Comparison of some scalarization methods in multiobjective optimization, Bulletin of the Malaysian Mathematical Sciences Society, 42, 1875, 10.1007/s40840-017-0579-4 Kraus, 2011, Multi-objective selection for collecting cluster alternatives, Computational Statistics, 26, 341, 10.1007/s00180-011-0244-6 Krawczyk, 2013, Accuracy and diversity in classifier selection for one-class classification ensembles, 46 B. Krawczyk, M. Woźniak, Optimization algorithms for one-class classification ensemble pruning, in: Asian Conference on Intelligent Information and Database Systems, No. Part II, 2014, pp. 127–136. Krstajic, 2014, Cross-validation pitfalls when selecting and assessing regression and classification models, Journal of Cheminformatics, 6, 1, 10.1186/1758-2946-6-10 Kulaif, 2013, Improved regularization in extreme learning machines Larochelle, 2007, An empirical evaluation of deep architectures on problems with many factors of variation, International Conference on Machine Learning, 2006, 473, 10.1145/1273496.1273556 Löfström, 2009, Ensemble member selection using multi-objective optimization, 245 Mao, 2013, Model selection of extreme learning machine based on multi-objective optimization, Neural Computing and Applications, 22, 521, 10.1007/s00521-011-0804-2 Marler, 2004, Survey of multi-objective optimization methods for engineering, Structural and Multidisciplinary Optimization, 26, 369, 10.1007/s00158-003-0368-6 Miettinen, 1999 Miranda, 2014, A hybrid meta-learning architecture for multi-objective optimization of SVM parameters, Neurocomputing, 143, 27, 10.1016/j.neucom.2014.06.026 Miranda, 2012, Combining a multi-objective optimization approach with meta-learning for SVM parameter selection, 2909 Mukhopadhyay, 2009, Multiobjective genetic clustering with ensemble among pareto front solutions: application to MRI brain image segmentation, 236 Nag, 2016, A multiobjective genetic programming-based ensemble for simultaneous feature selection and classification, IEEE Transactions on Cybernetics, 46, 499, 10.1109/TCYB.2015.2404806 A.A.F. Neto, A.M.P. Canuto, T.B. Ludermir, Using good and bad diversity measures in the design of ensemble systems: a genetic algorithm approach, in: IEEE Congress on Evolutionary Computation, 2013, pp. 789–796. Ojha, 2017, Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming, Applied Soft Computing Journal, 52, 909, 10.1016/j.asoc.2016.09.035 L.S. Oliveira, M. Morita, R. Sabourin, F. Bortolozzi, Multi-objective genetic algorithms to create ensemble of classifiers, in: Lecture Notes in Computer Science, vol. 3410, 2005, pp. 592–606. Pilat, 2013, Multiobjectivization for classifier parameter tuning, 97 Raimundo, 2020, An extension of the non-inferior set estimation algorithm for many objectives, European Journal of Operational Research, 284, 53, 10.1016/j.ejor.2019.11.017 J.D.M. Rennie, Regularized Logistic Regression is Strictly Convex, 2005, http://people.csail.mit.edu/jrennie/writing Rosales-Pérez, 2015, Surrogate-assisted multi-objective model selection for support vector machines, Neurocomputing, 150, 163, 10.1016/j.neucom.2014.08.075 Saha, 2016, A multiobjective based automatic framework for classifying cancer-microRNA biomarkers, Gene Reports, 4, 91, 10.1016/j.genrep.2016.04.001 Schapire, 2003, Measures of diversity in classifier ensembles, Machine Learning, 51, 181, 10.1023/A:1022859003006 Schapire, 2009, A short introduction to boosting, Journal of Japanese Society for Artificial Intelligence, 14, 771 Skillings, 1981, On the use of a Friedman-type in balanced statistic block designs and unbalanced, Technometrics, 23, 171, 10.1080/00401706.1981.10486261 Smith, 2014, Evolutionary multi-objective generation of recurrent neural network ensembles for time series prediction, Neurocomputing, 143, 302, 10.1016/j.neucom.2014.05.062 Wainberg, 2016, Are random forests truly the best classifiers?, Journal of Machine Learning Research, 17, 1 Wang, 2014, A multi-objective ensemble method for online class imbalance learning, 3311 C. Weihs, K. Luebke, I. Czogiel, Response Surface Methodology for Optimizing Hyper Parameters, TU Dortmund Technical Report, 2005. Yin, 2018, Robust multinomial logistic regression based on RPCA, IEEE Journal on Selected Topics in Signal Processing, 12, 1144, 10.1109/JSTSP.2018.2872460 G.G.b. Zhang, J. Yin, S. Zhang, L.L. Cheng, Regularization based ordering for ensemble pruning, in: International Conference on Fuzzy Systems and Knowledge Discovery, vol. 2, 2011, pp. 1325–1329. Zhao, 2018, Multiobjective sparse ensemble learning by means of evolutionary algorithms, Decision Support Systems, 111, 86, 10.1016/j.dss.2018.05.003 Zheng, 2013, Lazy paired hyper-parameter tuning, 1924 Zhou, 2012