S. Kotsiantis

Georgios Kostopoulos, Sotiris Kotsiantis, Omiros Ragos, 2017, 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA).

Investigating cluster validation metrics for optimal number of clusters determination

Charalampos M. Liapis, S. Kotsiantis, Aikaterini Karanikola, 2021, Intell. Decis. Technol..

A combinational incremental ensemble of classifiers as a technique for predicting students' performance in distance education

Sotiris B. Kotsiantis, Michalis Nik Xenos, Kiriakos Patriarcheas, 2010, Knowl. Based Syst..

PREDICTING STUDENTS' PERFORMANCE IN DISTANCE LEARNING USING MACHINE LEARNING TECHNIQUES

Sotiris B. Kotsiantis, Panayiotis E. Pintelas, Christos Pierrakeas, 2004, Appl. Artif. Intell..

Semi-supervised regression: A recent review

Georgios Kostopoulos, Stamatis Karlos, Sotiris Kotsiantis, 2018, J. Intell. Fuzzy Syst..

Bitcoin Price Prediction Combining Data and Text Mining

Sotiris Kotsiantis, Pantelis Linardatos, S. Kotsiantis, 2020 .

An effective LA approach to predict student achievement

Vassilios S. Verykios, Sotiris Kotsiantis, Andreas F. Gkontzis, 2018, PCI.

An incrementally updateable ensemble learner

Stamatis Karlos, Sotiris Kotsiantis, Aikaterini Karanikola, 2018, PCI.

A Semi-supervised regressor based on model trees

Nikos Fazakis, Kyriakos N. Sgarbas, Stamatis Karlos, 2018, SETN.

An incremental self-trained ensemble algorithm

Nikos Fazakis, Vasileios G. Kanas, Stamatis Karlos, 2018, 2018 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS).

Bagging different instead of similar models for regression and classification problems

Sotiris B. Kotsiantis, Dimitris N. Kanellopoulos, S. Kotsiantis, 2010, Int. J. Comput. Appl. Technol..

Local rotation forest of decision stumps for regression problems

P. E. Pintelas, S. B. Kotsiantis, S. Kotsiantis, 2009, 2009 2nd IEEE International Conference on Computer Science and Information Technology.

Local dagging of decision stumps for regression and classification problems

D.S. Anyfantis, M.G. Karagiannopoulos, S.B. Kotsiantis, 2007, 2007 Mediterranean Conference on Control & Automation.

Using Data Mining Techniques for Estimating Minimum, Maximum and Average Daily Temperature Values

Sotiris Kotsiantis, Athanassios A. Argiriou, S. Lykoudis, 2007 .

A Fast Ensemble of Regressors

Panayiotis E. Pintelas, Sotiris Kotsiantis, M. Karagiannopoulos, 2007 .

Bagged Averaging of Regression Models

Sotiris B. Kotsiantis, Dimitris Kanellopoulos, Ioannis D. Zaharakis, 2006, AIAI.

Local Additive Regression of Decision Stumps

Sotiris B. Kotsiantis, Dimitris Kanellopoulos, Panayiotis E. Pintelas, 2006, SETN.

Local Boosting of Decision Stumps for Regression and Classification Problems

Sotiris B. Kotsiantis, Dimitris Kanellopoulos, Panayiotis E. Pintelas, 2006, J. Comput..

Integrating global and local application of naive bayes classifier

Sotiris B. Kotsiantis, S. Kotsiantis, 2014, Int. Arab J. Inf. Technol..

Machine learning: a review of classification and combining techniques

Sotiris B. Kotsiantis, Panayiotis E. Pintelas, Ioannis D. Zaharakis, 2006, Artificial Intelligence Review.

Preventing Student Dropout in Distance Learning Using Machine Learning Techniques

Sotiris B. Kotsiantis, Panayiotis E. Pintelas, Christos Pierrakeas, 2003, KES.

Iterative Robust Semi-Supervised Missing Data Imputation

Georgios Kostopoulos, Nikos Fazakis, Iosif Mporas, 2020, IEEE Access.

Explainable AI: A Review of Machine Learning Interpretability Methods

Sotiris Kotsiantis, Vasilis Papastefanopoulos, Pantelis Linardatos, 2020, Entropy.

COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population

Sotiris Kotsiantis, Vasilis Papastefanopoulos, Pantelis Linardatos, 2020, Applied Sciences.

Decision trees: a recent overview

Sotiris B. Kotsiantis, S. Kotsiantis, 2011, Artificial Intelligence Review.

Local Application of One-Level Trees

Panayiotis E. Pintelas, Sotiris Kotsiantis, M. Karagiannopoulos, 2007 .

Classification of acoustical signals by combining active learning strategies with semi-supervised learning schemes

Sotiris Kotsiantis, Vasileios G. Kanas, Stamatis Karlos, 2021, Neural Computing and Applications.

Active learning Rotation Forest for multiclass classification

Sotiris Kotsiantis, Stamatis Karlos, Vangjel Kazllarof, 2019, Comput. Intell..

Active fuzzy rule induction

Stamatis Karlos, Sotiris Kotsiantis, Aikaterini Karanikola, 2018, 2018 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS).

Combining bagging, boosting, rotation forest and random subspace methods

Sotiris B. Kotsiantis, S. Kotsiantis, 2011, Artificial Intelligence Review.

Implementing AutoML in Educational Data Mining for Prediction Tasks

Georgios Kostopoulos, Sotiris Kotsiantis, O. Ragos, 2019, Applied Sciences.

Ensemble of classifiers for handling biomedical problems

S. B. Kotsiantis, I. Tsagaraki, S. Kotsiantis, 2010, 2010 6th International Conference on Advanced Information Management and Service (IMS).

ON THE SELECTION OF CLASSIFIER-SPECIFIC FEATURE SELECTION ALGORITHMS

Panayiotis E. Pintelas, Sotiris Kotsiantis, S. Kotsiantis, 2004 .

A Multi-Method Survey on the Use of Sentiment Analysis in Multivariate Financial Time Series Forecasting

Sotiris Kotsiantis, Charalampos M. Liapis, Aikaterini Karanikola, 2021, Entropy.

An incremental ensemble of classifiers

Sotiris B. Kotsiantis, S. Kotsiantis, 2011, Artificial Intelligence Review.

Handling imbalanced datasets: A review

Dimitris Kanellopoulos, Panayiotis E. Pintelas, Sotiris Kotsiantis, 2006 .

Data Preprocessing for Supervised Leaning

Dimitris Kanellopoulos, Panayiotis E. Pintelas, Sotiris Kotsiantis, 2007 .

Multi-Objective Evolutionary Optimization Algorithms for Machine Learning: A Recent Survey

Michael N. Vrahatis, Sotiris Kotsiantis, Christos K. Aridas, 2019, Approximation and Optimization.

A novel explainable image classification framework: case study on skin cancer and plant disease prediction

Sotiris Kotsiantis, Ioannis E. Livieris, Panagiotis Pintelas, 2021, Neural Computing and Applications.

Data preprocessing in predictive data mining

Sotiris B. Kotsiantis, Michael N. Vrahatis, Stamatios-Aggelos N. Alexandropoulos, 2019, The Knowledge Engineering Review.

Increasing the accuracy of incremental naive bayes classifier using instance based learning

Sotiris Kotsiantis, S. Kotsiantis, 2013 .

Local rotation-based ensemble

Sotiris B. Kotsiantis, S. Kotsiantis, 2010, Int. J. Knowl. Eng. Data Min..

Local Random Subspace Method for Constructing Multiple Decision Stumps

S.B. Kotsiantis, S. Kotsiantis, 2009, 2009 International Conference on Information and Financial Engineering.

Selective costing voting for bankruptcy prediction

Sotiris B. Kotsiantis, Dimitris Tzelepis, Euaggelos Koumanakos, 2007, Int. J. Knowl. Based Intell. Eng. Syst..

Feature Selection for Regression Problems

M. Karagiannopoulos, D. Anyfantis, P. E. Pintelas, 2007 .

Local voting of weak classifiers

Sotiris B. Kotsiantis, Panayiotis E. Pintelas, S. Kotsiantis, 2005, Int. J. Knowl. Based Intell. Eng. Syst..

Local Bagging of Decision Stumps

Sotiris B. Kotsiantis, Panayiotis E. Pintelas, George E. Tsekouras, 2005, IEA/AIE.

Supervised Machine Learning: A Review of Classification Techniques

Sotiris B. Kotsiantis, S. Tsumoto, S. Kotsiantis, 2007, Informatica.

Combining Bagging and Boosting

Panayiotis E. Pintelas, Sotiris Kotsiantis, S. Kotsiantis, 2007 .

Stacking Strong Ensembles of Classifiers

Sotiris B. Kotsiantis, Michael N. Vrahatis, Christos K. Aridas, 2019, AIAI.

Self-Trained LMT for Semisupervised Learning

Sotiris B. Kotsiantis, Nikos Fazakis, Kyriakos N. Sgarbas, 2015, Comput. Intell. Neurosci..

Fuzzy-based Active Learning for Predicting Student Academic Performance

Georgios Kostopoulos, Omiros Ragos, Sotiris Kotsiantis, 2020, Proceedings of the 6th International Conference on Engineering & MIS 2020.

Hybrid local boosting utilizing unlabeled data in classification tasks

Sotiris B. Kotsiantis, Michael N. Vrahatis, Christos K. Aridas, 2017, Evolving Systems.

Combining Prototype Selection with Local Boosting

Sotiris B. Kotsiantis, Michael N. Vrahatis, Christos K. Aridas, 2016, AIAI.

Predicting students marks in Hellenic Open University

Sotiris B. Kotsiantis, Panayiotis E. Pintelas, S. Kotsiantis, 2005, Fifth IEEE International Conference on Advanced Learning Technologies (ICALT'05).

Predicting the Response of Laminated Composite Beams: A Comparison of Machine Learning Algorithms

S. Kotsiantis, A. Charalampakis, G. Tsiatas, 2022, Frontiers in Built Environment.

Integrating global and local application of random subspace ensemble

Sotiris B. Kotsiantis, S. Kotsiantis, 2014, J. Intell. Fuzzy Syst..

Transfer Learning from Deep Neural Networks for Predicting Student Performance

Georgios Kostopoulos, Sotiris Kotsiantis, Omiros Ragos, 2020, Applied Sciences.

Machine Learning Solution Artificial Neural Networks Human Tracking and Profiling Classification of Kashmiri Language

Omar A. Alzubi, Khalid Nazim Abdul Sattar, S. Berretti, 2022 .

Bagging and boosting variants for handling classifications problems: a survey

Sotiris B. Kotsiantis, S. Kotsiantis, 2013, The Knowledge Engineering Review.

A Semi-Supervised Regression Algorithm for Grade Prediction of Students in Distance Learning Courses

Georgios Kostopoulos, Nikos Fazakis, Sotiris Kotsiantis, 2019, Int. J. Artif. Intell. Tools.

Editorial: Applications of Fuzzy Systems in Data Science and Big Data

Gautam Srivastava, Chun-Wei Lin, Dragan Pamucar, 2021, IEEE Trans. Fuzzy Syst..

Towards Intelligent Wireless Web Services for Tourism

Sotiris Kotsiantis, Dimitris Kanellopoulos, S. Kotsiantis, 2006 .

Uncertainty Based Under-Sampling for Learning Naive Bayes Classifiers Under Imbalanced Data Sets

Sotiris B. Kotsiantis, Nikos Fazakis, Vasileios G. Kanas, 2020, IEEE Access.

Explainable Machine Learning Framework for Image Classification Problems: Case Study on Glioma Cancer Prediction

Ioannis E. Livieris, Panagiotis Pintelas, Sotiris Kotsiantis, 2020, J. Imaging.

Polarity, emotions and online activity of students and tutors as features in predicting grades

Dimitrios Kalles, Vassilios S. Verykios, Christos Panagiotakopoulos, 2020, Intell. Decis. Technol..

Association Rules Mining: A Recent Overview

Dimitris Kanellopoulos, Sotiris Kotsiantis, S. Kotsiantis, 2006 .

A comparison of the optimized LSTM, XGBOOST and ARIMA in Time Series forecasting

Sotiris Kotsiantis, Aikaterini Karanikola, Iliana Paliari, 2021, 2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA).

Investigating Deep Stock Market Forecasting with Sentiment Analysis

S. Kotsiantis, Aikaterini Karanikola, C. M. Liapis, 2023, Entropy.

A Comparison of Contemporary Methods on Univariate Time Series Forecasting

S. Kotsiantis, Aikaterini Karanikola, C. M. Liapis, 2021, Learning and Analytics in Intelligent Systems.

An ensemble forecasting method using univariate time series COVID-19 data

S. Kotsiantis, Aikaterini Ch. Karanikola, C. M. Liapis, 2020, PCI.

Energy Load Forecasting: Investigating Mid-Term Predictions with Ensemble Learners

S. Kotsiantis, Aikaterini Karanikola, C. M. Liapis, 2022, AIAI.

Semantic Web: A state of the art survey

Dimitris Kanellopoulos, Sotiris Kotsiantis, Δημήτριος Κανελλόπουλος, 2007 .

Integrating Global and Local Application of Discriminative Multinomial Bayesian Classifier for Text Classification

Sotiris B. Kotsiantis, Emmanuel Pappas, S. Kotsiantis, 2012, ISI.

RETRACTED ARTICLE: Feature selection for machine learning classification problems: a recent overview

S B Kotsiantis, S. Kotsiantis, 2014, Artificial Intelligence Review.

Evaluating and recommending Greek newspapers' websites using clustering

Dimitris Kanellopoulos, Sotiris Kotsiantis, S. Kotsiantis, 2012, Program.

Mixture of Expert Agents for Handling Imbalanced Data Sets

Panayiotis E. Pintelas, Sotiris Kotsiantis, S. Kotsiantis, 2003 .

Combination of Active Learning and Semi-Supervised Learning under a Self-Training Scheme

Nikos Fazakis, Vasileios G. Kanas, Stamatis Karlos, 2019, Entropy.

Ontology-based Learning Applications: A Development Methodology

Sotiris B. Kotsiantis, Dimitris Kanellopoulos, Panayiotis E. Pintelas, 2006, IASTED Conf. on Software Engineering.

A multi-scheme semi-supervised regression approach

Nikos Fazakis, Kyriakos N. Sgarbas, Stamatis Karlos, 2019, Pattern Recognit. Lett..

Predicting University Students' Grades Based on Previous Academic Achievements

Georgios Kostopoulos, Christos Pierrakeas, Sotiris Kotsiantis, 2018, 2018 9th International Conference on Information, Intelligence, Systems and Applications (IISA).

Combining Bagging, Boosting and Dagging for Classification Problems

Sotiris B. Kotsiantis, Dimitris Kanellopoulos, S. Kotsiantis, 2007, KES.

A decision support prototype tool for predicting student performance in an ODL environment

Sotiris B. Kotsiantis, Panayiotis E. Pintelas, S. Kotsiantis, 2004, Interact. Technol. Smart Educ..

Recent Advances in Clustering : A Brief Survey

S. Kotsiantis, P. Pintelas, 2004 .

Increasing the Classification Accuracy of Simple Bayesian Classifier

Sotiris B. Kotsiantis, Panayiotis E. Pintelas, S. Kotsiantis, 2004, AIMSA.

A Soft-Voting Ensemble Based Co-Training Scheme Using Static Selection for Binary Classification Problems

Georgios Kostopoulos, Stamatis Karlos, Sotiris Kotsiantis, 2020, Algorithms.

A new ensemble method for outlier identification

Sotiris B. Kotsiantis, Michael N. Vrahatis, Stamatios-Aggelos N. Alexandropoulos, 2020, 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence).

Financial Fraudulent Statements Detection through a Deep Dense Artificial Neural Network

Sotiris B. Kotsiantis, Michael N. Vrahatis, Stamatios-Aggelos N. Alexandropoulos, 2019, 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA).

Integrating global and local boosting

Michael N. Vrahatis, Sotiris Kotsiantis, Anastasia-Dimitra Lipitakis, 2015, 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA).

Combining ensembles algorithms of symbolic learners

Sotiris Kotsiantis, Anastasia-Dimitra Lipitakis, S. Kotsiantis, 2015, 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA).

A hybrid Machine Learning methodology for imbalanced datasets

Sotiris Kotsiantis, Anastasia-Dimitra Lipitakis, S. Kotsiantis, 2014, IISA 2014, The 5th International Conference on Information, Intelligence, Systems and Applications.

INTEGRATING GLOBAL AND LOCAL VOTING OF CLASSIFIERS

Sotiris B. Kotsiantis, S. Kotsiantis, 2012, Cybern. Syst..

Use of machine learning techniques for educational proposes: a decision support system for forecasting students’ grades

S. Kotsiantis, 2012, Artificial Intelligence Review.

Using learning analytics to identify successful learners in a blended learning course

Sotiris Kotsiantis, Vassilis Komis, Nikolaos Tselios, 2013 .

Forecasting Fraudulent Financial Statements using Data Mining

Sotiris Kotsiantis, E. Koumanakos, Dimitris Tzelepis, 2007 .

Bagging Model Trees for Classification Problems

Sotiris B. Kotsiantis, Panayiotis E. Pintelas, George E. Tsekouras, 2005, Panhellenic Conference on Informatics.

A Cost Sensitive Technique for Ordinal Classification Problems

Sotiris B. Kotsiantis, Panayiotis E. Pintelas, S. Kotsiantis, 2004, SETN.

Mining Association Rules in Large Database by Implementing Pipelining Technique in Partition Algorithm

Kanhaiya Lal, N. C. Mahanti, P. Tan, 2010 .

Multiview Learning for Early Prognosis of Academic Performance: A Case Study

Georgios Kostopoulos, Sotiris Kotsiantis, Stamatis Karlos, 2019, IEEE Transactions on Learning Technologies.

Machine learning and nonlinear models for the estimation of fundamental period of vibration of masonry infilled RC frame structures

Sotiris Kotsiantis, George C. Tsiatas, Aristotelis E. Charalampakis, 2020 .

Robustness of learning techniques in handling class noise in imbalanced datasets

Sotiris B. Kotsiantis, Panayiotis E. Pintelas, M. Karagiannopoulos, 2007, AIAI.

Effectiveness of semi-supervised learning in bankruptcy prediction

Nikos Fazakis, Kyriakos N. Sgarbas, Stamatis Karlos, 2016, 2016 7th International Conference on Information, Intelligence, Systems & Applications (IISA).

Bank provision reversals and income smoothing: A case study

S. Kotsiantis, E. Aggelopoulos, Antonios Georgopoulos, 2022, Journal of Accounting and Public Policy.

CST-Voting: A semi-supervised ensemble method for classification problems

Sotiris B. Kotsiantis, Georgios Kostopoulos, Ioannis E. Livieris, 2018, J. Intell. Fuzzy Syst..

Investigating the Benefits of Exploiting Incremental Learners Under Active Learning Scheme

Nikos Fazakis, Vasileios G. Kanas, Stamatis Karlos, 2019, AIAI.

Early Dropout Prediction in MOOCs through Supervised Learning and Hyperparameter Optimization

Georgios Kostopoulos, Sotiris Kotsiantis, Achilles Kameas, 2021, Electronics.

Automated hand gesture recognition for educational applications

Stamatis Karlos, Sotiris Kotsiantis, Vangjel Kazllarof, 2016, PCI.

Increasing the accuracy of Hidden Naive Bayes model

Sotiris Kotsiantis, Vasilis Tampakas, S. Kotsiantis, 2010, 2010 6th International Conference on Advanced Information Management and Service (IMS).

Selective costing ensemble for handling imbalanced data sets

Sotiris B. Kotsiantis, Panayiotis E. Pintelas, S. Kotsiantis, 2009, Int. J. Hybrid Intell. Syst..

Local Grading of Learners

Sotiris B. Kotsiantis, S. Kotsiantis, 2008, 2008 Panhellenic Conference on Informatics.

Local Selective Voting

S. Kotsiantis, D. Kanellopoulos, S. Kotsiantis, 2007, 2007 International Conference on Convergence Information Technology (ICCIT 2007).

Hybrid local boosting utilizing unlabeled data in classification tasks

Christos K. Aridas, M. N. Vrahatis, S. Kotsiantis, 2019, Evol. Syst..

Self-Trained Stacking Model for Semi-Supervised Learning

Nikos Fazakis, Kyriakos N. Sgarbas, Stamatis Karlos, 2017, Int. J. Artif. Intell. Tools.

Bagging Random Trees for Estimation of Tissue Softness

Sotiris B. Kotsiantis, Panayiotis E. Pintelas, George E. Tsekouras, 2005, MLDM.

Unsupervised Outlier Detection: A Meta-Learning Algorithm Based on Feature Selection

S. Kotsiantis, Pantelis Linardatos, Vasilis Papastefanopoulos, 2021, Electronics.

Random Resampling in the One-Versus-All Strategy for Handling Multi-class Problems

Sotiris B. Kotsiantis, Michael N. Vrahatis, Christos K. Aridas, 2017, EANN.

Speaker Identification Using Semi-supervised Learning

Sotiris B. Kotsiantis, Nikos Fazakis, Kyriakos N. Sgarbas, 2015, SPECOM.

Assessing Supervised Machine Learning Techniques for Predicting Student Learning Preferences

Sotiris Kotsiantis, I. D. Zaharakis, P. E. Pintelas, 2002 .

Locally application of cascade generalization for classification problems

Sotiris B. Kotsiantis, S. Kotsiantis, 2008, Intell. Decis. Technol..

Stacking Cost Sensitive Models

Sotiris B. Kotsiantis, S. Kotsiantis, 2008, 2008 Panhellenic Conference on Informatics.

A Hybrid Decision Support Tool - Using Ensemble of Classifiers

Sotiris B. Kotsiantis, Panayiotis E. Pintelas, S. Kotsiantis, 2004, ICEIS.

A multi-view-CNN framework for deep representation learning in image classification

Emmanuel G. Pintelas, S. Kotsiantis, I. Livieris, 2023, Comput. Vis. Image Underst..

Internet and Multimedia Communications

Dimitris Kanellopoulos, Sotiris Kotsiantis, Panayotis Pintelas, 2009 .

Combining Active Learning with Self-train algorithm for classification of multimodal problems

Nikos Fazakis, Sotiris Kotsiantis, Vasileios G. Kanas, 2019, 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA).

A multivariate ensemble learning method for medium-term energy forecasting

S. Kotsiantis, Aikaterini Karanikola, C. M. Liapis, 2023, Neural Computing and Applications.

Temporal Convolutional Networks and BERT-Based Multi-Label Emotion Analysis for Financial Forecasting

S. Kotsiantis, C. M. Liapis, 2023, Inf..

A generic sparse regression imputation method for time series and tabular data

K. M. Giannoutakis, S. Kotsiantis, G. Gravvanis, 2023, Knowl. Based Syst..

A Semisupervised Cascade Classification Algorithm

Sotiris B. Kotsiantis, Nikos Fazakis, Kyriakos N. Sgarbas, 2016, Appl. Comput. Intell. Soft Comput..

Temporal Dynamics of Citizen-Reported Urban Challenges: A Comprehensive Time Series Analysis

Andreas F. Gkontzis, S. Kotsiantis, V. Verykios, 2024, Big Data and Cognitive Computing.

Enhancing Urban Resilience: Smart City Data Analyses, Forecasts, and Digital Twin Techniques at the Neighborhood Level

Andreas F. Gkontzis, S. Kotsiantis, V. Verykios, 2024, Future Internet.

Fuzzy Clustering of Categorical Attributes and its Use in Analyzing Cultural Data

Sotiris B. Kotsiantis, Panayiotis E. Pintelas, George E. Tsekouras, 2007, International Conference on Computational Intelligence.

Multivariate Time-Series Forecasting: A Review of Deep Learning Methods in Internet of Things Applications to Smart Cities

S. Kotsiantis, Pantelis Linardatos, Vasilis Papastefanopoulos, 2023, Smart Cities.

CO2 concentration forecasting in smart cities using a hybrid ARIMA–TFT model on multivariate time series IoT data

S. Kotsiantis, Pantelis Linardatos, Vasilis Papastefanopoulos, 2023, Scientific reports.

University students’ intention to use search engines for research purposes: A structural equation modeling approach

Sotiris Kotsiantis, Konstantinos Lavidas, Anthi Achriani, 2019, Education and Information Technologies.

Text Classification Using Machine Learning Techniques

S. Kotsiantis, M. Ikonomakis, V. Tampakas, 2005 .

Combining random forest and support vector machines for semi-supervised learning

Sotiris Kotsiantis, Christos K. Aridas, Christos Aridas, 2015, Panhellenic Conference on Informatics.

Rotation forest of random subspace models

Christos K. Aridas, Stamatios-Aggelos N. Alexandropoulos, S. Kotsiantis, 2022, Intell. Decis. Technol..

Financial Application of Multi-Instance Learning: Two Greek Case Studies

Sotiris B. Kotsiantis, Dimitris Kanellopoulos, Vasilis Tampakas, 2010, J. Convergence Inf. Technol..

Cascade Generalization with Classification and Model Trees

S. Kotsiantis, D. Kanellopoulos, S. Kotsiantis, 2008, 2008 Third International Conference on Convergence and Hybrid Information Technology.

Combining Bagging and Additive Regression

Sotiris Kotsiantis, S. Kotsiantis, 2007 .

Financial Application of Neural Networks: Two Case Studies in Greece

Sotiris B. Kotsiantis, Dimitris Tzelepis, Euaggelos Koumanakos, 2006, ICANN.

Local averaging of heterogeneous regression models

Sotiris B. Kotsiantis, S. Kotsiantis, 2006, Int. J. Hybrid Intell. Syst..

Bagged Voting Ensembles

Sotiris B. Kotsiantis, Panayiotis E. Pintelas, S. Kotsiantis, 2004, AIMSA.

On Implementing a Financial Decision Support System

Sotiris Kotsiantis, Dimitris Kanellopoulos, V. Tampakas, 2006 .

Energy Efficient Wireless Sensor Network using Genetic Algorithm based Association Rules

P. Priakanth, T. Abirami, S. Kotsiantis, 2014 .

Fuzzy-based active learning for predicting student academic performance using autoML: a step-wise approach

Georgios Kostopoulos, Sotiris Kotsiantis, Maria Tsiakmaki, 2021, Journal of Computing in Higher Education.

A comprehensive review on zero-shot-learning techniques

Aristidis G. Vrahatis, S. Kotsiantis, Konstantinos Lazaros, 2024, Intelligent Decision Technologies.