| Machine learning tools for long-term type 2 diabetes risk prediction N Fazakis, O Kocsis, E Dritsas, S Alexiou, N Fakotakis, K Moustakas ieee Access 9, 103737-103757, 2021 | 108 | 2021 |
| Uncertainty based under-sampling for learning naive bayes classifiers under imbalanced data sets CK Aridas, S Karlos, VG Kanas, N Fazakis, SB Kotsiantis IEEE Access 8, 2122-2133, 2019 | 70 | 2019 |
| Self‐Trained LMT for Semisupervised Learning N Fazakis, S Karlos, S Kotsiantis, K Sgarbas Computational intelligence and neuroscience 2016 (1), 3057481, 2016 | 44 | 2016 |
| Iterative robust semi-supervised missing data imputation N Fazakis, G Kostopoulos, S Kotsiantis, I Mporas IEEE Access 8, 90555-90569, 2020 | 38 | 2020 |
| A semi-supervised regression algorithm for grade prediction of students in distance learning courses G Kostopoulos, S Kotsiantis, N Fazakis, G Koutsonikos, C Pierrakeas International Journal on Artificial Intelligence Tools 28 (04), 1940001, 2019 | 30 | 2019 |
| Long-term Cholesterol Risk Prediction using Machine Learning Techniques in ELSA Database. N Fazakis, E Dritsas, O Kocsis, N Fakotakis, K Moustakas IJCCI, 445-450, 2021 | 26 | 2021 |
| Long-term hypertension risk prediction with ml techniques in elsa database E Dritsas, N Fazakis, O Kocsis, N Fakotakis, K Moustakas Learning and Intelligent Optimization: 15th International Conference, LION …, 2021 | 26 | 2021 |
| Combination of active learning and semi-supervised learning under a self-training scheme N Fazakis, VG Kanas, CK Aridas, S Karlos, S Kotsiantis Entropy 21 (10), 988, 2019 | 21 | 2019 |
| A multi-scheme semi-supervised regression approach N Fazakis, S Karlos, S Kotsiantis, K Sgarbas Pattern Recognition Letters 125, 758-765, 2019 | 20 | 2019 |
| Optimal team pairing of elder office employees with machine learning on synthetic data E Dritsas, N Fazakis, O Kocsis, K Moustakas, N Fakotakis 2021 12th International Conference on Information, Intelligence, Systems …, 2021 | 14 | 2021 |
| Combining active learning with self-train algorithm for classification of multimodal problems S Karlos, VG Kanas, C Aridas, N Fazakis, S Kotsiantis 2019 10th International Conference on Information, Intelligence, Systems and …, 2019 | 14 | 2019 |
| Speaker identification using semi-supervised learning N Fazakis, S Karlos, S Kotsiantis, K Sgarbas Speech and Computer: 17th International Conference, SPECOM 2015, Athens …, 2015 | 13 | 2015 |
| Locally application of naive Bayes for self-training S Karlos, N Fazakis, AP Panagopoulou, S Kotsiantis, K Sgarbas Evolving Systems 8, 3-18, 2017 | 12 | 2017 |
| Self-trained rotation forest for semi-supervised learning N Fazakis, S Karlos, S Kotsiantis, K Sgarbas Journal of Intelligent & Fuzzy Systems 32 (1), 711-722, 2017 | 12 | 2017 |
| Effectiveness of semi-supervised learning in bankruptcy prediction S Karlos, S Kotsiantis, N Fazakis, K Sgarbas 2016 7th International Conference on Information, Intelligence, Systems …, 2016 | 12 | 2016 |
| A semisupervised cascade classification algorithm S Karlos, N Fazakis, S Kotsiantis, K Sgarbas Applied Computational Intelligence and Soft Computing 2016 (1), 5919717, 2016 | 10 | 2016 |
| Self-trained stacking model for semi-supervised learning S Karlos, N Fazakis, S Kotsiantis, K Sgarbas International journal on artificial intelligence tools 26 (02), 1750001, 2017 | 9 | 2017 |
| Self-trained extreme gradient boosting trees N Fazakis, G Kostopoulos, S Karlos, S Kotsiantis, K Sgarbas 2019 10th international conference on information, Intelligence, Systems and …, 2019 | 8 | 2019 |
| Semi-supervised forecasting of fraudulent financial statements S Karlos, N Fazakis, S Kotsiantis, K Sgarbas Proceedings of the 20th Pan-Hellenic Conference on Informatics, 1-6, 2016 | 8 | 2016 |
| Self-train logitboost for semi-supervised learning S Karlos, N Fazakis, S Kotsiantis, K Sgarbas Engineering Applications of Neural Networks: 16th International Conference …, 2015 | 8 | 2015 |
Sotiris KotsiantisAssociate Professor, Department of Mathematics, University of Patras, GreeceVerified email at math.upatras.gr
