NEC orchestrating a brighter world
NEC Laboratories Europe

Artificial Intelligence Innovation

A. Goyal, J. Khiari: "Diversity-Aware Weighted Majority Vote Classifier for Imbalanced Data", International Joint Conference on Neural Networks (IJCNN) 2020.

M. Schmidt, J. Gastinger, S. Nicolas, A. Schuelke: "HAMLET - A Learning Curve-Enabled Multi-Armed Bandit for Algorithm Selection", International Joint Conference on Neural Networks (IJCNN) 2020.

L. Moreira-Matias, A. Saadallah, R. Sousa, J. Khiari, E. Jenelius and J. Gama, "BRIGHT - Drift-Aware Demand Predictions for Taxi Networks", IEEE Transactions on Knowledge and Data Engineering, February 2020.

K. Gkiotsalitis, F. Alesiani, “Robust timetable optimization for bus lines subject to resource and regulatory constraints”, Transportation Research Part E: Logistics and Transportation Review, June 2019

F. Alesiani, G. Ermis: "How to increase modal shift of freight transport towards public transport network", 30th European Conference on Operational Research (EURO 2019), June 2019

M. Schmidt, S. Safarani, J. Gastinger, T. Jacobs, S. Nicolas, A. Schuelke: "On the Performance of Differential Evolution for Hyperparameter Tuning", International Joint Conference on Neural Networks 2019. April 2019

X. He, F. Alesiani, A. Shaker, "Efficient and Scalable Multi-task Regression on Massive Number of Tasks", The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 19), February 2019.

V. Cerqueira, L. Moreira-Matias, J. Khiari, H. Van Lint, "On Evaluating Floating Car Data Quality for Knowledge  Discovery", IEEE Transactions on Intelligent Transportation Systems, October 2018

F. Alesiani, L. Moreira-Matias, M. Faizrahnemoon: "On Learning from Inaccurate and Incomplete Traffic Flow Data IEEE Transactions on Intelligent Transportation Systems", October 2018

J. Khiari, L. Moreira-Matias, A. Shaker, B. Ženko, and S. Džeroski: "MetaBags: Bagged Meta-Decision Trees for Regression", The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 2018

Top of this page