| Autoencoder-based deep metric learning for network intrusion detection G Andresini, A Appice, D Malerba Information Sciences 569, 706-727, 2021 | 148 | 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 | 142 | 2019 |
| 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 | 135 | 2003 |
| 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 | 134 | 2004 |
| 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 | 130 | 2020 |
| 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 | 127 | 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 | 119 | 2021 |
| Nearest cluster-based intrusion detection through convolutional neural networks G Andresini, A Appice, D Malerba Knowledge-Based Systems 216, 106798, 2021 | 104 | 2021 |
| 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 | 100 | 2015 |
| Activity prediction of business process instances with inception CNN models N Di Mauro, A Appice, TMA Basile AI* IA 2019–Advances in Artificial Intelligence: XVIIIth International …, 2019 | 89 | 2019 |
| Stepwise induction of multi-target model trees A Appice, S Džeroski European conference on machine learning, 502-509, 2007 | 86 | 2007 |
| Mr-SBC: a multi-relational naive bayes classifier M Ceci, A Appice, D Malerba Knowledge Discovery in Databases: PKDD 2003: 7th European Conference on …, 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 | 71 | 2003 |
| 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 | 64 | 2021 |
| 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 | 64 | 2004 |
| Network regression with predictive clustering trees D Stojanova, M Ceci, A Appice, S Džeroski Data Mining and Knowledge Discovery 25, 378-413, 2012 | 63 | 2012 |
| 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 | 61 | 2013 |
| 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 | 59 | 2020 |
| Empowering a GIS with inductive learning capabilities: the case of INGENS D Malerba, F Esposito, A Lanza, FA Lisi, A Appice Computers, Environment and Urban Systems 27 (3), 265-281, 2003 | 57 | 2003 |
| Predictive process mining meets computer vision V Pasquadibisceglie, A Appice, G Castellano, D Malerba Business Process Management Forum: BPM Forum 2020, Seville, Spain, September …, 2020 | 56 | 2020 |
Donato MalerbaProfessor of Computer Science, Dept. of Computer Science, University of Bari "Aldo Moro", ItalyEmail verificata su uniba.it
