| Autoencoder-based deep metric learning for network intrusion detection G Andresini, A Appice, D Malerba Information Sciences 569, 706-727, 2021 | 185 | 2021 |
| Insomnia: Towards concept-drift robustness in network intrusion detection G Andresini, F Pendlebury, F Pierazzi, C Loglisci, A Appice, L Cavallaro Proceedings of the 14th ACM workshop on artificial intelligence and security …, 2021 | 182 | 2021 |
| Using convolutional neural networks for predictive process analytics V Pasquadibisceglie, A Appice, G Castellano, D Malerba 2019 international conference on process mining (ICPM), 129-136, 2019 | 170 | 2019 |
| GAN augmentation to deal with imbalance in imaging-based intrusion detection G Andresini, A Appice, L De Rose, D Malerba Future Generation Computer Systems 123, 108-127, 2021 | 159 | 2021 |
| Multi-channel deep feature learning for intrusion detection G Andresini, A Appice, N Di Mauro, C Loglisci, D Malerba IEEE Access 8, 53346-53359, 2020 | 153 | 2020 |
| Top-down induction of model trees with regression and splitting nodes D Malerba, F Esposito, M Ceci, A Appice IEEE Transactions on Pattern Analysis and Machine Intelligence 26 (5), 612-625, 2004 | 142 | 2004 |
| Discovery of spatial association rules in geo-referenced census data: A relational mining approach A Appice, M Ceci, A Lanza, FA Lisi, D Malerba Intelligent Data Analysis 7 (6), 541-566, 2003 | 136 | 2003 |
| Nearest cluster-based intrusion detection through convolutional neural networks G Andresini, A Appice, D Malerba Knowledge-based systems 216, 106798, 2021 | 121 | 2021 |
| Activity prediction of business process instances with inception CNN models N Di Mauro, A Appice, TMA Basile International conference of the italian association for artificial …, 2019 | 111 | 2019 |
| A co-training strategy for multiple view clustering in process mining A Appice, D Malerba IEEE transactions on services computing 9 (6), 832-845, 2015 | 106 | 2015 |
| Stepwise induction of multi-target model trees A Appice, S Džeroski European conference on machine learning, 502-509, 2007 | 89 | 2007 |
| A multi-view deep learning approach for predictive business process monitoring V Pasquadibisceglie, A Appice, G Castellano, D Malerba IEEE Transactions on Services Computing 15 (4), 2382-2395, 2021 | 84 | 2021 |
| Mr-SBC: a multi-relational naive bayes classifier M Ceci, A Appice, D Malerba European conference on principles of data mining and knowledge discovery, 95-106, 2003 | 77 | 2003 |
| Mining spatial association rules in census data D Malerba, F Esposito, FA Lisi, A Appice Research in Official Statistics. v5 i1, 19-44, 2003 | 73 | 2003 |
| A multi-stage machine learning approach to predict dengue incidence: a case study in Mexico A Appice, YR Gel, I Iliev, V Lyubchich, D Malerba Ieee Access 8, 52713-52725, 2020 | 70 | 2020 |
| Dealing with spatial autocorrelation when learning predictive clustering trees D Stojanova, M Ceci, A Appice, D Malerba, S Džeroski Ecological Informatics 13, 22-39, 2013 | 68 | 2013 |
| ROULETTE: A neural attention multi-output model for explainable network intrusion detection G Andresini, A Appice, FP Caforio, D Malerba, G Vessio Expert Systems with Applications 201, 117144, 2022 | 67 | 2022 |
| Network regression with predictive clustering trees D Stojanova, M Ceci, A Appice, S Džeroski Data Mining and Knowledge Discovery 25 (2), 378-413, 2012 | 66 | 2012 |
| Redundant feature elimination for multi-class problems A Appice, M Ceci, S Rawles, P Flach Proceedings of the twenty-first international conference on Machine learning, 5, 2004 | 66 | 2004 |
| Predictive process mining meets computer vision V Pasquadibisceglie, A Appice, G Castellano, D Malerba International Conference on Business Process Management, 176-192, 2020 | 61 | 2020 |
Donato MalerbaProfessor of Computer Science, Dept. of Computer Science, University of Bari "Aldo Moro", ItalyVerified email at uniba.it
