School of Informatics AUTh
B. Liu, J. Wang, K. Sun, G. Tsoumakas (2023), Fine-grained selective similarity integration for drug–target interaction prediction, Briefings in Bioinformatics
N. Mylonas, I. Mollas, B. Liu, Y. Manolopoulos and G. Tsoumakas, "On the Persistence of Multilabel Learning, Its Recent Trends, and Its Open Issues," in IEEE Intelligent Systems, vol. 38, no. 2, pp. 28-31, March-April 2023, doi: 10.1109/MIS.2023.3255591.
Bin Liu, Dimitrios Papadopoulos, Fragkiskos D Malliaros, Grigorios Tsoumakas, Apostolos N Papadopoulos, Multiple similarity drug–target interaction prediction with random walks and matrix factorization, Briefings in Bioinformatics, Volume 23, Issue 5, September 2022, bbac353, https://doi.org/10.1093/bib/bbac353
Liu Bin, Konstantinos Blekas, and Grigorios Tsoumakas. "Multi-label sampling based on local label imbalance." Pattern Recognition 122 (2022): 108294.
Liu, Bin, Pliakos, Konstantinos, Vens, Celine, and Tsoumakas, Grigorios "Drug-target interaction prediction via an ensemble of weighted nearest neighbors with interaction recovery." Applied Intelligence (2021): 1-23.
B. Liu, K. Pliakos, C. Vens, G. Tsoumakas. (2020) Local Imbalance based Ensemble for Predicting Interactions between Novel Drugs and Targets. Machine Learning for Pharma and Healthcare Applications Workshop (PharML 2020)
B. Liu, G. Tsoumakas. Dealing with Class Imbalance in Classifier Chains via Random Undersampling. Knowledge-Based Systems 192 (2020): 105292.
B. Liu, G. Tsoumakas, (2019) Synthetic Oversampling of Multi-label Data Based on Local Label Distribution, 2019 Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2019), Würzburg, Germany.
Bin Liu, Grigorios Tsoumakas, (2019) Synthetic Oversampling of Multi-Label Data based on Local Label Distribution: Supplementary Material, ECML-PKDD
B. Liu, G. Tsoumakas, (2018) Making Classifier Chains Resilient to Class Imbalance, 10th Asian Conference on Machine Learning (ACML 2018), Beijing, China