Evolving multi-user fuzzy classifier systems integrating human uncertainty and expert knowledge

Information Sciences - Tập 596 - Trang 30-52 - 2022
Edwin Lughofer1
1Institute for Mathematical Methods in Medicine and Data Based Modeling, Johannes Kepler University Linz, Austria

Tài liệu tham khảo

Akerkar, 2009 Angelov, 2010 Angelov, 2018, Deep rule-based classifier with human-level performance and characteristics, Information Sciences, 463–464, 196, 10.1016/j.ins.2018.06.048 Angelov, 2018, Autonomous learning multimodel systems from data streams, IEEE Transactions on Fuzzy Systems, 26, 2213, 10.1109/TFUZZ.2017.2769039 Angelov, 2018, A generalized methodology for data analysis, IEEE Transactions on Cybernetics, 48, 2981, 10.1109/TCYB.2017.2753880 Angelov, 2008, Evolving fuzzy classifiers using different model architectures, Fuzzy Sets and Systems, 159, 3160, 10.1016/j.fss.2008.06.019 Angelov, 2012, A new type of simplified fuzzy rule-based system, International Journal of General Systems, 41, 163, 10.1080/03081079.2011.634807 Angelov, 2008, Evolving fuzzy-rule-based classifiers from data streams, IEEE Transactions on Fuzzy Systems, 16, 1462, 10.1109/TFUZZ.2008.925904 Angelov, 2012 Bifet, 2010, MOA: Massive online analysis, Journal of Machine Learning Research, 11, 1601 Breiman, 2001, Random forests, Machine Learning, 45, 5, 10.1023/A:1010933404324 K. Brinker. On active learning in multi-label classification. In Myra Spiliopoulou, Rudolf Kruse, Christian Borgelt, Andreas Nürnberger, and Wolfgang Gaul, editors, From Data and Information Analysis to Knowledge Engineering, pages 206–213, Berlin, Heidelberg, 2006. Springer, Berlin Heidelberg. Caleb-Solly, 2007, Adaptive surface inspection via interactive evolution, Image and Vision Computing, 25, 1058, 10.1016/j.imavis.2006.04.023 Casillas, 2003 Castillo, 2007 B.S. Costa, P.P. Angelov, and L.A. Guedes. Fully unsupervised fault detection and identification based on recursive density estimation and self-evolving cloud-based classifier. Neurocomputing, 150(A):289–303, 2015. Cruz-Sandoval, 2019, Semi-automated data labeling for activity recognition in pervasive healthcare, Sensors, 19, 3035, 10.3390/s19143035 de Campos Souza, 2020, Fuzzy neural networks and neuro-fuzzy networks: A review the main techniques and applications used in the literature, Applied Soft Computing, 92, 10.1016/j.asoc.2020.106275 de Campos Souza, 2021, An advanced interpretable fuzzy neural network model based on uni-nullneuron constructed from n-uninorms P.V. de Campos Souza and E. Lughofer. An evolving neuro-fuzzy system based on uni-nullneurons with advanced interpretability capabilities. Neurocomputing, 451(231–251), 2021. Deemter, 2010 F. Dosilovic, M. Brcic, and N. Hlupic. Explainable artificial intelligence: A survey. In Proceedings of the 41st International Convention Proceedings, MIPRO 2018, pages 210–215, Opatija, Croatia, 2018. Dovzan, 2015, Implementation of an evolving fuzzy model (eFuMo) in a monitoring system for a waste-water treatment process, IEEE Transactions on Fuzzy Systems, 23, 1761, 10.1109/TFUZZ.2014.2379252 Eitzinger, 2010, Assessment of the influence of adaptive components in trainable surface inspection systems, Machine Vision and Applications, 21, 613, 10.1007/s00138-009-0211-1 Gacto, 2011, Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures, Information Sciences, 181, 4340, 10.1016/j.ins.2011.02.021 Gama, 2010 Garcia, 2020, Incremental missing-data imputation for evolving fuzzy granular prediction, IEEE Transactions on Fuzzy Systems, 28, 2348, 10.1109/TFUZZ.2019.2935688 Gegov, 2017, Aggregation of inconsistent rules for fuzzy rule base simplification, International Journal of Knowledge-based and Intelligent Engineering Systems, 21, 135, 10.3233/KES-170358 Gray, 1984, Vector quantization, IEEE ASSP Magazine, 1, 4, 10.1109/MASSP.1984.1162229 Hatzilygeroudis, 2004, Integrating (rules, neural networks) and cases for knowledgerepresentation and reasoning in expert systems, Expert Systems with Applications, 27, 63, 10.1016/j.eswa.2003.12.004 Hofmann, 2013 Holzinger, 2016, Interactive machine learning for health informatics: when do we need the human-in-the-loop?, Brain Informatics, 3, 118, 10.1007/s40708-016-0042-6 Kangin, 2016, Autonomously evolving classifier TEDAClass, Information Sciences, 366, 1, 10.1016/j.ins.2016.05.012 Klement, 2000 Kocijan, 2019 Kose, 2015, An interactive machine-learning-based electronic fraud and abusedetection system in healthcare insurance, Applied Soft Computing, 36, 283, 10.1016/j.asoc.2015.07.018 Krempl, 2015, Optimised probabilistic active learning (OPAL), Machine Learning, 100, 449, 10.1007/s10994-015-5504-1 Krishnamoorthy, 2009 Kuncheva, 2000 Kuncheva, 2004 H. Kwon, G.D. Abowd, and T. Plötz. Handling annotation uncertainty in human activity recognition. In Proceedings of the 23rd International Symposium on Wearable Computers, ISWC ’19, pages 109–117, 2019. Leite, 2012, Evolving fuzzy granular modeling from nonstationary fuzzy data streams, Evolving Systems, 3, 65, 10.1007/s12530-012-9050-9 Leite, 2020, Optimal rule-based granular systems from data streams, IEEE Transactions on Fuzzy Systems, 28, 583, 10.1109/TFUZZ.2019.2911493 Lemos, 2013, Adaptive fault detection and diagnosis using an evolving fuzzy classifier, Information Sciences, 220, 64, 10.1016/j.ins.2011.08.030 Lughofer, 2008, Extensions of vector quantization for incremental clustering, Pattern Recognition, 41, 995, 10.1016/j.patcog.2007.07.019 Lughofer, 2012, Single-pass active learning with conflict and ignorance, Evolving Systems, 3, 251, 10.1007/s12530-012-9060-7 Lughofer, 2013, On-line assurance of interpretability criteria in evolving fuzzy systems — achievements, new concepts and open issues, Information Sciences, 251, 22, 10.1016/j.ins.2013.07.002 Lughofer, 2016, Evolving fuzzy systems — fundamentals, reliability, interpretability and useability, 67 Lughofer, 2017, On-line active learning: A new paradigm to improve practical useability of data stream modeling methods, Information Sciences, 415–416, 356, 10.1016/j.ins.2017.06.038 Lughofer, 2013, Reliable all-pairs evolving fuzzy classifiers, IEEE Transactions on Fuzzy Systems, 21, 625, 10.1109/TFUZZ.2012.2226892 Lughofer, 2015, Generalized smart evolving fuzzy systems, Evolving Systems, 6, 269, 10.1007/s12530-015-9132-6 Lughofer, 2017, Explaining classifier decisions linguistically for stimulating and improving operators labeling behavior, Information Sciences, 420, 16, 10.1016/j.ins.2017.08.012 Lughofer, 2015, Autonomous data stream clustering implementing incremental split-and-merge techniques — towards a plug-and-play approach, Information Sciences, 204, 54, 10.1016/j.ins.2015.01.010 Lughofer, 2019 Montiel, 2018, Scikit-multiflow: A multi-output streaming framework, Journal of Machine Learning Research, 19, 1 Nauck, 1998, NEFCLASS-X – a soft computing tool to build readable fuzzy classifiers, BT Technology Journal, 16, 180, 10.1023/A:1009610822227 M. Olave, V. Rajkovic, and M. Bohanec. An application for admission in public school systems. Expert Systems in Public Administration, pages 145–160, 1989. Pedrycz, 2007 Pedrycz, 2008 Pratama, 2015, Recurrent classifier based on an incremental meta-cognitive scaffolding algorithm, IEEE Transactions on Fuzzy Systems, 23, 2048, 10.1109/TFUZZ.2015.2402683 Pratama, 2015, pClass: An effective classifier for streaming examples, IEEE Transactions on Fuzzy Systems, 23, 369, 10.1109/TFUZZ.2014.2312983 Pratama, 2021, Scalable teacher forcing network for semi-supervised large scale data streams, Information Sciences, 576, 407, 10.1016/j.ins.2021.06.075 Pratama, 2016, Scaffolding type-2 classifier for incremental learning under concept drifts, Neurocomputing, 191, 304, 10.1016/j.neucom.2016.01.049 Pratama, 2018, Evolving ensemble fuzzy classifier, IEEE Transactions on Fuzzy Systems, 26, 2552, 10.1109/TFUZZ.2018.2796099 Pratama, 2020, An incremental construction of deep neuro fuzzy system for continual learning of nonstationary data streams, IEEE Transactions on Fuzzy Systems, 28, 1315 Quost, 2007, Pairwise classifier combination using belief functions, Pattern Recognition Letters, 28, 644, 10.1016/j.patrec.2006.11.002 Ravikumara, 2011, Machine learning approach for automated visual inspection of machine components, Expert Sytems with Applications, 38, 3260, 10.1016/j.eswa.2010.09.012 Raykar, 2010, Learning from crowds, Journal of Machine Learning Research, 11, 1297 Rigatos, 2016 Rutkowska, 2003, Multi-expert systems, volume 3019, 650 Samek, 2019 Sayed-Mouchaweh, 2015 Sayed-Mouchaweh, 2018 Schapire, 2013, Explaining adaboost, 37 Schapire, 2014 Settles, 2012, Active Learning, Morgan & Claypool Publishers Sheng, 2008, Get another label? improving data quality and data mining using multiple, noisy labelers, 614 Siler, 2005 Skrjanc, 2019, Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A survey, Information Sciences, 490, 344, 10.1016/j.ins.2019.03.060 Subramanian, 2013, A metacognitive neuro-fuzzy inference system (mcfis) for sequential classification problems, IEEE Transactions on Fuzzy Systems, 21, 1080, 10.1109/TFUZZ.2013.2242894 Tabata, 2010, Data compression by volume prototypes for streaming data, Pattern Recognition, 43, 3162, 10.1016/j.patcog.2010.03.012 M.G. Tsipouras, T.P. Exarchos, and D.I. Fotiadis. Integration of global and local knowledge for fuzzy expert system creation: application to arrhythmic beat classification. In Proceedings of the Annual International IEEE Conference on Eng Med Biol Soc. 2007. Pubmed Gov, 2007. Tung, 2013, eT2FIS: An evolving type-2 neural fuzzy inference system, Information Sciences, 220, 124, 10.1016/j.ins.2012.02.031 Valizadegan, 2013, Learning classification models from multiple experts, Journal of Biomedical Informatics, 46, 1125, 10.1016/j.jbi.2013.08.007 Vetterlein, 2012, Vagueness: where degree-based approaches are useful, and where we can do without, Soft Computing, 16, 1833, 10.1007/s00500-012-0834-4 Wang, 2016, Ambiguity-based multiclass active learning, IEEE Transactions on Fuzzy Systems, 24, 242, 10.1109/TFUZZ.2015.2451698 Ware, 2001, Interactive machine learning: letting users build classifiers, International Journal of Human-Computer Studies, 55, 281, 10.1006/ijhc.2001.0499 Yam, 2000, Representing membership functions as points in high-dimensional spaces for fuzzy interpolation and extrapolation, IEEE Trans. on Fuzzy Systems, 8, 761, 10.1109/91.890335 Zhang, 2019, Online adaptive asymmetric active learning with limited budgets, IEEE Transactions on Knowledge and Data Engineering, 33, 2680, 10.1109/TKDE.2019.2955078 Zliobaite, 2014, Active learning with drifting streaming data, IEEE Transactions on Neural Networks and Learning Systems, 25, 27, 10.1109/TNNLS.2012.2236570