| Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning G Lemaitre, F Nogueira, CK Aridas Journal of Machine Learning Research 18 (17), 1-5, 2017 | 2999 | 2017 |
| 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, 2020 | 73 | 2020 |
| Multi-Objective Evolutionary Optimization Algorithms for Machine Learning: A Recent Survey SAN Alexandropoulos, CK Aridas, SB Kotsiantis, MN Vrahatis Springer Optimization and Its Applications 145, 35-55, 2019 | 54 | 2019 |
| Stacking strong ensembles of classifiers SAN Alexandropoulos, CK Aridas, SB Kotsiantis, MN Vrahatis Artificial Intelligence Applications and Innovations: 15th IFIP WG 12.5 …, 2019 | 41 | 2019 |
| A deep dense neural network for bankruptcy prediction SAN Alexandropoulos, CK Aridas, SB Kotsiantis, MN Vrahatis Engineering Applications of Neural Networks: 20th International Conference …, 2019 | 29 | 2019 |
| 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 | 23 | 2019 |
| Classification of acoustical signals by combining active learning strategies with semi-supervised learning schemes S Karlos, C Aridas, VG Kanas, S Kotsiantis Neural Computing and Applications, 1-18, 2023 | 15 | 2023 |
| 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 |
| Evaluating MASHAP as a faster alternative to LIME for model-agnostic machine learning interpretability A Messalas, C Aridas, Y Kanellopoulos 2020 IEEE International Conference on Big Data (Big Data), 5777-5779, 2020 | 12 | 2020 |
| Random resampling in the one-versus-all strategy for handling multi-class problems CK Aridas, SAN Alexandropoulos, SB Kotsiantis, MN Vrahatis Engineering Applications of Neural Networks: 18th International Conference …, 2017 | 9 | 2017 |
| Combining random forest and support vector machines for semi-supervised learning C Aridas, S Kotsiantis Proceedings of the 19th Panhellenic Conference on Informatics, 123-128, 2015 | 9 | 2015 |
| A tool supported framework for the assessment of algorithmic accountability E Tagiou, Y Kanellopoulos, C Aridas, C Makris 2019 10th International Conference on Information, Intelligence, Systems and …, 2019 | 5 | 2019 |
| Hybrid local boosting utilizing unlabeled data in classification tasks CK Aridas, SB Kotsiantis, MN Vrahatis Evolving Systems 10 (1), 51-61, 2017 | 4 | 2017 |
| Combining prototype selection with local boosting CK Aridas, SB Kotsiantis, MN Vrahatis Artificial Intelligence Applications and Innovations: 12th IFIP WG 12.5 …, 2016 | 3 | 2016 |
| Investigating the benefits of exploiting incremental learners under active learning scheme S Karlos, VG Kanas, N Fazakis, C Aridas, S Kotsiantis Artificial Intelligence Applications and Innovations: 15th IFIP WG 12.5 …, 2019 | 2 | 2019 |
| Imbalanced dataset for benchmarking G Lemaitre, F Nogueira, CK Aridas, DVR Oliveira Zenodo, 2016 | 2 | 2016 |
| Rotation forest of random subspace models SAN Alexandropoulos, CK Aridas, SB Kotsiantis, GA Gravvanis, ... Intelligent Decision Technologies, 1-10, 2022 | | 2022 |
| Towards Discrimination-Free Classification via Fairness-Aware Bagging P Papagiannopoulos, C Aridas, Y Kanellopoulos, C Makris Proceedings of the 25th Pan-Hellenic Conference on Informatics, 184-189, 2021 | | 2021 |
| PyThia: A Reporting Tool on Bias Evaluation and Mitigation Y Kanellopoulos, C Aridas 4th Workshop on Mechanism Design for Social, 2020 | | 2020 |
| vfi: Classification by Voting Feature Intervals in Python CK Aridas Zenodo, 2020 | | 2020 |
