School of Informatics AUTh
Spolaôr, N., Monard, M.C., Tsoumakas, G., Lee, H.D. (2015) A systematic review of multi-label feature selection and a new method based on label construction, Neurocomputing, Volume 180, pp. 3-15.
E. Spyromitros-Xioufis, G. Tsoumakas, W. Groves, I. Vlahavas (2016) Multi-Target Regression via Input Space Expansion: Treating Targets as Inputs. Machine Learning Journal 104(1), 55-98.
I. Kavakiotis, A. Xochelli, A. Agathangelidis, G. Tsoumakas, N. Maglaveras, K. Stamatopoulos, A. Hadzidimitriou, I. Vlahavas, I. Chouvarda (2016) Integrating Multiple Immunogenetic Data Sources For Feature Extraction and Mining Mutation Patterns: The Case of Chronic Lymphocytic Leukemia Shared Mutations BMC Bioinformatics 17 (5), 375
I. Kavakiotis, A. Triantafyllidis, D. Ntelidou, P. Alexandri, HJ. Megens, RP. Crooijmans, MA. Groenen, G. Tsoumakas, I. Vlahavas (2015) "TRES: Identification of Discriminatory and Informative SNPs from Population Genomic Data.", Journal of Heredity. 2015 Sep-Oct;106(5):672-6.
F. Markatopoulou, G. Tsoumakas, I. Vlahavas (2015) “Dynamic Ensemble Pruning based on Multi-Label Classification”, Neurocomputing, Elsevier, Volume 150, Part B, pp. 501-512.
E. Spyromitros-Xioufis, S. Papadopoulos, I. Kompatsiaris, G. Tsoumakas, I. Vlahavas, “A Comprehensive Study over VLAD and Product Quantization in Large-scale Image Retrieval”, IEEE Transactions on Multimedia, IEEE, 2014.
A. Fachantidis, I. Partalas, G. Tsoumakas, I. Vlahavas, “Transferring Task Models in Reinforcement Learning Agents”, Neurocomputing, Elsevier, 107, pp. 23-32, 2013.
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.
