School of Informatics AUTh
G. Tzanis, I. Kavakiotis, I. Vlahavas, “PolyA-iEP: A Data Mining Method for the Effective Prediction of Polyadenylation Sites”, Expert Systems with Applications, Elsevier, 38(10), pp. 1239812408, 2011.
G. Tsoumakas, E. Spyromitros-Xioufis, J. Vilcek, I. Vlahavas, “Mulan: A Java Library for Multi-Label Learning”, Journal of Machine Learning Research, 12, pp. 2411-2414, 2011.
G. Tsoumakas, I. Katakis, I. Vlahavas, “Random k-Labelsets for Multi-Label Classification”, IEEE Transactions on Knowledge and Data Engineering, IEEE, 23(7), pp. 1079-1089, 2011.
I. Partalas, G. Tsoumakas, I. Vlahavas (2010) “An Ensemble Uncertainty Aware Measure for Directed Hill Climbing Ensemble Pruning”, Machine Learning 81(3), pp. 257-282.
I. Katakis, G. Tsoumakas, I. Vlahavas, “Tracking Recurring Contexts using Ensemble Classifiers: An Application to Email Filtering”, Knowledge and Information Systems, Springer, 22(3), pp. 371-391, 2010.
E. Hatzikos, J. Hatonen, N. Bassiliades, I. Vlahavas, E. Fournou, “Applying adaptive prediction to sea-water quality measurements”, Expert Systems with Applications, Elsevier, Vol. 36, Iss. 3, Part 2, pp. 6773-6779, 2009.
I. Partalas, G. Tsoumakas, I. Vlahavas (2009) “Pruning an Ensemble of Classifiers via Reinforcement Learning”, Neurocomputing, Elsevier, 72(7-9), pp. 1900-1909.
I. Katakis, G. Tsoumakas, E. Banos, N. Bassiliades, I. Vlahavas, “An Adaptive Personalized News Dissemination System”, Journal of Intelligent Information Systems, Springer, 32 (2), pp. 191-212, 2009.
I. Partalas, I. Feneris, I. Vlahavas, “A Hybrid Multiagent Reinforcement Learning Approach using Strategies and Fusion”, International Journal of Artificial Intelligence Tools (IJAIT), World Scientific, 17 (5), pp. 945-961, 2008.
I. Partalas, G. Tsoumakas, E. Hatzikos, I. Vlahavas (2008) “Greedy Regression Ensemble Selection: Theory and an Application to Water Quality”, Information Sciences, Elsevier, 178(20), pp. 3867-3879.
