| Central moment discrepancy (CMD) for domain-invariant representation learning W Zellinger, T Grubinger, E Lughofer, T Natschläger, S Saminger-Platz arXiv preprint arXiv:1702.08811, 2017 | 841 | 2017 |
| Evolving fuzzy systems-methodologies, advanced concepts and applications E Lughofer Springer, 2011 | 513 | 2011 |
| FLEXFIS: A robust incremental learning approach for evolving Takagi–Sugeno fuzzy models ED Lughofer IEEE Transactions on fuzzy systems 16 (6), 1393-1410, 2008 | 390 | 2008 |
| Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A survey I Škrjanc, JA Iglesias, A Sanchis, D Leite, E Lughofer, F Gomide Information sciences 490, 344-368, 2019 | 365 | 2019 |
| PANFIS: A novel incremental learning machine M Pratama, SG Anavatti, PP Angelov, E Lughofer IEEE Transactions on Neural Networks and Learning Systems 25 (1), 55-68, 2013 | 301 | 2013 |
| Evolving fuzzy classifiers using different model architectures P Angelov, E Lughofer, X Zhou Fuzzy sets and systems 159 (23), 3160-3182, 2008 | 240 | 2008 |
| Handling drifts and shifts in on-line data streams with evolving fuzzy systems E Lughofer, P Angelov Applied Soft Computing 11 (2), 2057-2068, 2011 | 217 | 2011 |
| Learning in non-stationary environments: methods and applications M Sayed-Mouchaweh, E Lughofer Springer Science & Business Media, 2012 | 213 | 2012 |
| GENEFIS: Toward an effective localist network M Pratama, SG Anavatti, E Lughofer IEEE Transactions on Fuzzy Systems 22 (3), 547-562, 2013 | 202 | 2013 |
| Extensions of vector quantization for incremental clustering E Lughofer Pattern recognition 41 (3), 995-1011, 2008 | 202 | 2008 |
| Generalized smart evolving fuzzy systems E Lughofer, C Cernuda, S Kindermann, M Pratama Evolving systems 6 (4), 269-292, 2015 | 193 | 2015 |
| An incremental learning of concept drifts using evolving type-2 recurrent fuzzy neural networks M Pratama, J Lu, E Lughofer, G Zhang, MJ Er IEEE Transactions on Fuzzy Systems 25 (5), 1175-1192, 2016 | 183 | 2016 |
| On-line assurance of interpretability criteria in evolving fuzzy systems–achievements, new concepts and open issues E Lughofer Information sciences 251, 22-46, 2013 | 160 | 2013 |
| pClass: an effective classifier for streaming examples M Pratama, SG Anavatti, M Joo, ED Lughofer IEEE Transactions on Fuzzy Systems 23 (2), 369-386, 2014 | 147 | 2014 |
| Hybrid active learning for reducing the annotation effort of operators in classification systems E Lughofer Pattern Recognition 45 (2), 884-896, 2012 | 146 | 2012 |
| Predictive maintenance in dynamic systems E Lughofer, M Sayed-Mouchaweh Springer International Publishing, 2019 | 145 | 2019 |
| Single-pass active learning with conflict and ignorance E Lughofer Evolving Systems 3 (4), 251-271, 2012 | 145 | 2012 |
| On-line elimination of local redundancies in evolving fuzzy systems E Lughofer, JL Bouchot, A Shaker Evolving systems 2 (3), 165-187, 2011 | 138 | 2011 |
| DEVDAN: Deep evolving denoising autoencoder A Ashfahani, M Pratama, E Lughofer, YS Ong Neurocomputing 390, 297-314, 2020 | 137 | 2020 |
| Autonomous data stream clustering implementing split-and-merge concepts–towards a plug-and-play approach E Lughofer, M Sayed-Mouchaweh Information Sciences 304, 54-79, 2015 | 135 | 2015 |
Mahardhika PratamaAssociate Professor & Enterprise Fellow, School of Computer Science and IT, Adelaide UniversityVerified email at adelaide.edu.au
