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Christos K. Aridas
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2020 – today
- 2023
- [j6]Stamatis Karlos
, Christos K. Aridas, Vasileios G. Kanas, Sotiris Kotsiantis:
Classification of acoustical signals by combining active learning strategies with semi-supervised learning schemes. Neural Comput. Appl. 35(1): 3-20 (2023) - 2022
- [j5]Stamatios-Aggelos N. Alexandropoulos, Christos K. Aridas, Sotiris B. Kotsiantis, George A. Gravvanis, Michael N. Vrahatis:
Rotation forest of random subspace models. Intell. Decis. Technol. 16(2): 315-324 (2022) - 2020
- [j4]Christos K. Aridas
Uncertainty Based Under-Sampling for Learning Naive Bayes Classifiers Under Imbalanced Data Sets. IEEE Access 8: 2122-2133 (2020)
2010 – 2019
- 2019
- [j3]Nikos Fazakis
Combination of Active Learning and Semi-Supervised Learning under a Self-Training Scheme. Entropy 21(10): 988 (2019) - [j2]Christos K. Aridas
Hybrid local boosting utilizing unlabeled data in classification tasks. Evol. Syst. 10(1): 51-61 (2019) - [c8]Stamatios-Aggelos N. Alexandropoulos, Christos K. Aridas
A Deep Dense Neural Network for Bankruptcy Prediction. EANN 2019: 435-444 - [c7]Stamatis Karlos
Investigating the Benefits of Exploiting Incremental Learners Under Active Learning Scheme. AIAI 2019: 37-49 - [c6]Stamatios-Aggelos N. Alexandropoulos, Christos K. Aridas
Stacking Strong Ensembles of Classifiers. AIAI 2019: 545-556 - [c5]Stamatis Karlos, Vasileios G. Kanas, Christos K. Aridas, Nikos Fazakis, Sotiris Kotsiantis:
Combining Active Learning with Self-train algorithm for classification of multimodal problems. IISA 2019: 1-8 - 2017
- [j1]Guillaume Lemaitre, Fernando Nogueira, Christos K. Aridas:
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning. J. Mach. Learn. Res. 18: 17:1-17:5 (2017) - [c4]Christos K. Aridas
Random Resampling in the One-Versus-All Strategy for Handling Multi-class Problems. EANN 2017: 111-121 - 2016
- [c3]Christos K. Aridas, Sotiris B. Kotsiantis
Increasing Diversity in Random Forests Using Naive Bayes. AIAI 2016: 75-86 - [c2]Christos K. Aridas
Combining Prototype Selection with Local Boosting. AIAI 2016: 94-105 - [i1]Guillaume Lemaitre, Fernando Nogueira, Christos K. Aridas:
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning. CoRR abs/1609.06570 (2016) - 2015
- [c1]Christos K. Aridas
Combining random forest and support vector machines for semi-supervised learning. Panhellenic Conference on Informatics 2015: 123-128
Coauthor Index
aka: Sotiris Kotsiantis
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