A ternary bitwise calculator based genetic algorithm for improving error correcting output codes

Aburomman, 2017, A novel weighted support vector machines multiclass classifier based on differential evolution for intrusion detection systems, Inf. Sci., 414, 225, 10.1016/j.ins.2017.06.007 Allwein, 2001, Reducing multiclass to binary: a unifying approach for margin classifiers, J. Mach. Learn. Res., 1, 113 Armstrong, 2002, Mll translocations specify a distinct gene expression profile that distinguishes a unique leukemia, Nat. Genet., 30, 41, 10.1038/ng765 Bagheri, 2013, A genetic-based subspace analysis method for improving Error-Correcting Output Coding, Pattern Recogn., 46, 2830, 10.1016/j.patcog.2013.03.014 Bautista, 2012, Minimal design of error-correcting output codes, Pattern Recogn. Lett., 33, 693, 10.1016/j.patrec.2011.09.023 Beer, 2002, Gene-expression profiles predict survival of patients with lung adenocarcinoma, Nat. Med., 8, 816, 10.1038/nm733 Cramme, 2002, On the learnability and design of output codes for multiclass problems, Mach. Learn., 47, 201, 10.1023/A:1013637720281 Dietterich, 1994, Solving multiclass learning problems via ECOCs, J. Artif. Intell. Res., 2, 263, 10.1613/jair.105 D. Dua, E. Karra Taniskidou, UCI machine learning repository, in: Irvine, CA: University of California, School of Information and Computer Science., http://archive.ics.uci.edu/ml, 2017. S. Escalera, O. Pujol, P. Radeva, ECOC-ONE: a novel coding and decoding strategy, in: Int C Patt Recog, 2006, pp. 578–581. Escalera, 2010, On the decoding process in ternary error-correcting output codes, IEEE Trans. Pattern Anal., 32, 120, 10.1109/TPAMI.2008.266 Escalera, 2009, Separability of ternary codes for sparse designs of error-correcting output codes, Pattern Recogn. Lett., 30, 285, 10.1016/j.patrec.2008.10.002 Feng, 2018, A novel ECOC algorithm with centroid distance based soft coding scheme GarciaPedrajas, 2008, Evolving output codes for multiclass problems, IEEE Trans. Evol. Comput., 12, 93, 10.1109/TEVC.2007.894201 GarcíaPedrajas, 2011, An empirical study of binary classifier fusion methods for multiclass classification, Inf. Fusion, 12, 111, 10.1016/j.inffus.2010.06.010 Iman, 1980, Approximations of the critical region of the fbietkan statistic, Commun. Stat. Theory Methods, 9, 571, 10.1080/03610928008827904 Jia, 2019, A multiphase cost-sensitive learning method based on the multiclass three-way decision-theoretic rough set model, Inf. Sci., 485, 248, 10.1016/j.ins.2019.01.067 Katuwal, 2020, Heterogeneous oblique random forest, Pattern Recogn., 99, 10.1016/j.patcog.2019.107078 Khan, 2001, Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks, Nat. Med., 7, 673, 10.1038/89044 Krawczyk, 2018, Dynamic ensemble selection for multi-class classification with one-class classifiers, Pattern Recogn., 83, 34, 10.1016/j.patcog.2018.05.015 La Cava, 2019, Multidimensional genetic programming for multiclass classification, Swarm Evol. Comput., 44, 260, 10.1016/j.swevo.2018.03.015 Li, 2019, A novel Error-Correcting Output Codes algorithm based on genetic programming, Swarm Evol. Comput., 50, 10.1016/j.swevo.2019.100564 Liu, 2016, A hierarchical ensemble of ECOC for cancer classification based on multi-class microarray data, Inf. Sci., 349, 102, 10.1016/j.ins.2016.02.028 Lorena, 2007, Evolutionary design of multiclass support vector machines, J. Intell. Fuzzy Syst., 18, 445 McDonald, 2014 Monti, 2003, Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data, Mach. Learn., 52, 91, 10.1023/A:1023949509487 Nazari, 2018, Securing templates in a face recognition system using Error-Correcting Output Code and chaos theory, Comput. Electr. Eng., 72, 644, 10.1016/j.compeleceng.2018.01.029 O’Brien, 2019, A random forests quantile classifier for class imbalanced data, Pattern Recogn., 90, 232, 10.1016/j.patcog.2019.01.036 ÖzöğürAkyüz, 2015, Pruning of Error Correcting Output Codes by optimization of accuracy–diversity trade off, Mach. Learn., 101, 253, 10.1007/s10994-014-5477-5 Pedregosa, 2011, Scikit-learn: machine learning in Python, J. Mach. Learn. Res., 12, 2825 Perou, 2000, Molecular portraits of human breast tumours, Nature, 406, 747, 10.1038/35021093 Pujol, 2006, Discriminant ECOC: a heuristic method for application dependent design of error correcting output codes, IEEE Trans. Pattern Anal. Mach. Intell., 28, 1007, 10.1109/TPAMI.2006.116 J. Qin, L. Liu, L. Shao, et al., Zero-Shot Action Recognition with Error-Correcting Output Codes, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1042–1051 Ramaswamy, 2001, Multiclass cancer diagnosis using tumor gene expression signatures, Proc. Nat. Acad. Sci., 98, 15149, 10.1073/pnas.211566398 Rodriguez, 2006, Rotation forest: a new classifier ensemble method, IEEE Trans. Pattern Anal., 28, 1619, 10.1109/TPAMI.2006.211 Simeone, 2012, Design of reject rules for ECOC classification systems, Pattern Recogn., 45, 863, 10.1016/j.patcog.2011.08.001 Statnikov, 2005, GEMS: a system for automated cancer diagnosis and biomarker discovery from microarray gene expression data, Int. J. Med. Inf., 74, 491, 10.1016/j.ijmedinf.2005.05.002 Su, 2001, Molecular classification of human carcinomas by use of gene expression signatures, Cancer Res., 61, 7388 M. Sun, K. Liu, Q. Hong, et al., A New ECOC Algorithm for Multiclass Microarray Data Classification, in: 24th International Conference on Pattern Recognition Beijing, China, 2018, pp. 454–458. Tamayo, 2002, Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning, Nat. Med., 8, 68, 10.1038/nm0102-68 Tang, 2019, A novel perspective on multiclass classification: regular simplex support vector machine, Inf. Sci., 480, 324, 10.1016/j.ins.2018.12.026 Tran, 2018, A LSTM based framework for handling multiclass imbalance in DGA botnet detection, Neurocomputing, 275, 2401, 10.1016/j.neucom.2017.11.018 Yeoh, 2002, Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling, Cancer Cell, 1, 133, 10.1016/S1535-6108(02)00032-6 H. Yu, KunHong Liu, Classification of Multi-Class Microarray Datasets Using A Minimizing Class-Overlapping Based ECOC Algorithm, in: ACM 5th International Conference on Bioinformatics and Computational Biology, ICBCB 2017, Hong Kong, China, 2017, pp. 51–54. Zhang, 2011, Data-driven intelligent transportation systems: a survey, IEEE Trans. Intell. Transp. Syst., 12, 1624, 10.1109/TITS.2011.2158001 Zhang, 2017, Benchmarking ensemble classifiers with novel co-trained kernel ridge regression and random vector functional link ensembles, IEEE Comput. Intell. Mag., 12, 61, 10.1109/MCI.2017.2742867 Zhang, 2015, Heuristic ternary error-correcting output codes via weight optimization and layered clustering-based approach, IEEE Trans. Cybern., 45, 289, 10.1109/TCYB.2014.2325603 Zhou, 2008, Data-driven decomposition for multi-class classification, Pattern Recogn., 41, 67, 10.1016/j.patcog.2007.05.020 Zhou, 2016, Constructing ECOC based on confusion matrix for multiclass learning problems, Sci. China, 59, 1 Zhou, 2017, One versus one multi-class classification fusion using optimizing decision directed acyclic graph for predicting listing status of companies, Inf. Fusion, 36, 80, 10.1016/j.inffus.2016.11.009 C. Zor, B. Yanikoglu, E. Merdivan, et al., BeamECOC: a local search for the optimization of the ECOC matrix, in: 2016 23rd International Conference on Pattern Recognition (ICPR), 2016, pp. 198–203.