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1.      S.Kotsiantis, C. Pierrakeas and P. Pintelas, Preventing student dropout in distance learning systems using machine learning techniques, Lecture Notes in Artificial Intelligence, KES 2003, Springer-Verlag Vol 2774, pp 267-274, 2003.

2.      S.Kotsiantis, P. Pintelas, Bagged Voting Ensembles, Lecture Notes in Artificial Intelligence, AIMSA 2004, Springer-Verlag Vol 3192, pp. 168-177, 2004.

3.      S.Kotsiantis, P. Pintelas, Increasing the Classification Accuracy of Simple Bayesian Classifier, Lecture Notes in Artificial Intelligence, AIMSA 2004, Springer-Verlag Vol 3192, pp. 198-207, 2004.

4.      S.Kotsiantis, P. Pintelas, A Cost Sensitive Technique for Ordinal Classification Problems, Lecture Notes in Artificial Intelligence, SETN 2004, Springer-Verlag Vol 3025, pp. 220-229,2004.

5.      S. Kotsiantis, D. Kanellopoulos, P. Pintelas, Multimedia Mining, WSEAS Transactions on Systems, Issue 10, Volume 3, December 2004, pp. 3263-3268.

6.      S. Kotsiantis, Pintelas, On the selection of classifier-specific feature selection algorithms, IJSIT Lecture Note of International Conference on Intelligent Knowledge Systems, Vol.1, No. 1, August 2004, pp 153-160.

7.      S. Kotsiantis, G. Tsekouras, P. Pintelas, Local Bagging of Decision Stumps, Lecture Notes in Artificial Intelligence, IEA/AIE 2005, Springer-Verlag Vol 3533, pp. 406 – 411, 2005.

8.      S. Kotsiantis, G. Tsekouras, C. Raptis, P. Pintelas, Modeling the organoleptic properties of matured wine distillates, Lecture Notes in Artificial Intelligence, MLDM 2005, Springer-Verlag, Vol. 3587, pp. 667 – 673, 2005.

9.      S. Kotsiantis, G. Tsekouras, P. Pintelas, Bagging random trees for estimation of tissue softness, Lecture Notes in Artificial Intelligence, MLDM 2005, Springer-Verlag, Vol. 3587, pp. 674 – 681, 2005.

10.  George E. Tsekouras, Dimitris Papageorgiou, Sotiris B. Kotsiantis, Christos Kalloniatis, Panagiotis E. Pintelas, A Fuzzy Logic-based Approach for Detecting Shifting Patterns in Cross- Cultural Data, Lecture Notes in Artificial Intelligence, IEA/AIE 2005, Springer-Verlag Vol 3533, pp. 705 – 708, 2005.

11.  S. Kotsiantis, G. Tsekouras, P. Pintelas, Bagging Model Trees for Classification Problems, Lecture Notes in Artificial Intelligence, PCI 2005, Springer-Verlag Vol 3746, pp. 328 – 337, 2005.

12.  S. Kotsiantis, E. Koumanakos, D. Tzelepis, V. Tampakas, Predicting Fraudulent Financial Statements with Machine Learning Techniques, Lecture Notes in Artificial Intelligence, SETN 2006, Springer-Verlag, Vol. 3955, pp. 538 – 542, 2006.

13.  S. Kotsiantis, D. Kanellopoulos, P. Pintelas, Local Additive Regression of Decision Stumps, Lecture Notes in Artificial Intelligence, Springer-Verlag, Vol. 3955, SETN 2006, pp. 148 – 157, 2006.

14.  S. Kotsiantis, Local Ordinal Classification, IFIP International Federation for Information Processing, Vol. 204, AIAI 2006, Springer-Verlag, pp 1-8, 2006.

15.  S. Kotsiantis, D. Kanellopoulos, I. Zaharakis, Bagged Averaging of Regression Models, IFIP International Federation for Information Processing, Vol. 204, AIAI 2006, Springer-Verlag, pp. 53-60, 2006.

16.  S. Kotsiantis, E. Koumanakos, D. Tzelepis, V. Tampakas, Financial Application of Neural Networks: Two case studies in Greece, Lecture Notes in Artificial Intelligence, ICANN 2006, Springer-Verlag, Vol. 4132, pp. 672 – 681, 2006.

17.  S. Kotsiantis, D. Kanellopoulos, Combining Bagging, Boosting and Dagging, Lecture Notes in Artificial Intelligence, KES2007, Springer-Verlag, Volume 4693/2007, pp. 493-500.

18.  D. Anyfantis, M. Karagiannopoulos, S. B. Kotsiantis and P. E. Pintelas, Robustness of learning algorithms in handling noise in imbalanced datasets, IFIP International Federation for Information Processing AIAI 2007, Vol. 247, Springer-Verlag, pp. 21-28.

19.  M. Karagiannopoulos, D. Anyfantis, S. B. Kotsiantis and P. E. Pintelas, A Wrapper for Reweighting Training Instances for Handling Imbalanced Datasets, IFIP International Federation for Information Processing AIAI 2007, Vol. 247, Springer-Verlag, pp. 29-36.

20.  S. B. Kotsiantis, P. E. Pintelas (2004), An Online Ensemble Of Classifiers, The Fourth International Workshop on Pattern Recognition in Information Systems – PRIS-2004, In conjunction with 6th International Conference on Enterprise Information Systems, pp 59-68, Porto - Portugal 14-17, April 2004.

21.  S.Kotsiantis, P. Pintelas (2004), A Hybrid Decision Support Tool, Proceedings of 6th International Conference on Enterprise Information Systems, Volume 2, pp 448-453, Porto - Portugal 14-17, April 2004.

22.  S. B. Kotsiantis, P. E. Pintelas (2004), Hybrid Feature Selection instead of Ensembles of Classifiers in Medical Decision Support, Proceedings of Information Processing and Management of Uncertainty in Knowledge-Based Systems, July 4-9, Perugia - Italy, pp. 269-276.

23.  S. Kotsiantis, P. Pintelas (2004), A Fast Ensemble of Classifiers, Proceedings of Information Processing and Management of Uncertainty in Knowledge-Based Systems, July 4-9, Perugia - Italy, pp. 277-284.

24.  S. Kotsiantis, P. Pintelas, Local Boosting of Weak Classifiers, IEEE 4th International Conference on Intelligent Systems Design and Applications (ISDA 2004), August 26-28, 2004, Budapest, Hungary, pp. 175-180.

25.  S. Kotsiantis, P. Pintelas, Selective Voting, IEEE 4th International Conference on Intelligent Systems Design and Applications (ISDA 2004), August 26-28, 2004, Budapest, Hungary, pp. 397-402.

26.  S. Kotsiantis, I. Zaharakis, V. Tampakas, P. Pintelas, On Constructing a Financial Decision Support System, Proceedings of International Conference on Enterprise Systems and Accounting 2004, September 3-4, 2004, Thessaloniki, Greece, pp 319-331.

27.  S. B. Kotsiantis, P.E. Pintelas, Predicting Students’ Marks in Hellenic Open University, Proceedings of 5th IEEE International Conference on Advanced Learning Technologies, July 5-8, 2005 Kaohsiung, Taiwan, pp. 664 - 668.

28.  S. Kotsiantis, D. Tzelepis, E. Koumanakos, V. Tampakas, Efficiency of Machine Learning Techniques in Bankruptcy Prediction, Proceedings of 2nd International Conference on Enterprise Systems and Accounting 2005, July 11-12, Thessaloniki, Greece, pp. 39-49.

29.  D. Kanellopoulos, S. Kotsiantis, P. Pintelas, Ontology-Based Learning Applications: A Development Methodology, Twenty-Fourth IASTED International Conference on SOFTWARE ENGINEERING, February 14 – 16, 2006 Innsbruck, Austria, pp.27-32.

30.  S. Kotsiantis, A. Kostoulas, S. Lykoudis, A. Argiriou, K. Menagias, Filling Missing Temperature Values in Weather Data Banks, 2nd IEE International Conference on Intelligent Environments, 5-6 July, 2006, Athens, Greece, Vol 1, pp. 327-334.

31.  D. Anyfantis, M. Karagiannopoulos, S. B. Kotsiantis and P. E. Pintelas, Local Dagging of Decision Stumps for Regression and Classification Problems, 15th IEEE Mediterranean Conference on Control and Automation. 27-30 June, 2007 Athens, Greece, CD Proceedings.

32.  M. Karagiannopoulos, D. Anyfantis, S. B. Kotsiantis and P. E. Pintelas, Local Cost Sensitive Learning For Handling Imbalanced Data Sets, 15th IEEE Mediterranean Conference on Control and Automation. 27-30 June, 2007 Athens, Greece, CD Proceedings.

33.  Dimitris Kanellopoulos, Sotiris Kotsiantis, Vasilis Tampakas, Towards an ontology-based system for intelligent prediction of firms with fraudulent financial statements, 12th IEEE Conference on Emerging Technologies and Factory Automation September 25-28, 2007 - Patras – Greece, pp.1300-1307.

34.  S. Kotsiantis, D. Kanellopoulos, Lazy MetaCost Naive Bayes, IEEE International Conference on Convergence Information Technology 2007 (ICCIT07), November 21st - 23rd 2007, Gyeongju, Korea, pp. 1602-1607.

35.  S. Kotsiantis, D. Kanellopoulos, Local Selective Voting, IEEE International Conference on Convergence Information Technology 2007 (ICCIT07), November 21st - 23rd 2007, Gyeongju, Korea, pp. 1621-1626.

36.  D. Anyfantis, M. Karagiannopoulos, S. B. Kotsiantis and P. E. Pintelas, Creating ensembles of classifiers by distributing an imbalance dataset to reach balance in each resulting training set, 2008 IEEE International Conference on Distributed Human-Machine Systems, Athens, Greece, March 9-12, 2008, pp. 311-315.

37.  S. B. Kotsiantis, P. E. Pintelas, Predicting Student Learning Preferences, 1th International Conference on Informatics, September 01-04,2004, Çesme, Turkey.

38.  S. Kotsiantis, Stacking Cost Sensitive Models, IEEE PCI 2008, August 28-30, 2008, Samos Island, Greece, 217-221.

39.  S. Kotsiantis, Local Grading of Learners, IEEE PCI 2008, August 28-30, 2008, Samos Island, Greece, pp. 209-213.

40.  S. Kotsiantis, D. Kanellopoulos, Applying Machine Learning Techniques for Environmental Reporting, 4th International Conference on Networked Computing and Advanced Information Management (NCM2008), IEEE CS, pp. 217-223, September 2nd-4th 2008, Gyeongju, Korea.

41.  S. Kotsiantis, D. Kanellopoulos, Grading Cost Sensitive Models, International Conference on Convergence and hybrid Information Technology (ICCIT08), IEEE CS, Nov. 11~13, 2008, Novotel Ambassador Busan, Busan, Korea, pp. 663-668.

42.  S. Kotsiantis, D. Kanellopoulos, Multi-instance learning for bankruptcy prediction, International Conference on Convergence and hybrid Information Technology (ICCIT08), IEEE CS, Nov. 11~13, 2008, Novotel Ambassador Busan, Busan, Korea, pp. 1007-1012.

43.  S. Kotsiantis, D. Kanellopoulos, Multi-Instance Learning for Predicting Fraudulent Financial Statements, International Conference on Convergence and hybrid Information Technology (ICCIT08), IEEE CS, Nov. 11~13, 2008, Novotel Ambassador Busan, Busan, Korea, pp. 448-452.

44.  S. Kotsiantis, D. Kanellopoulos, Cascade Generalization with Classification and Model Trees, International Conference on Convergence and hybrid Information Technology (ICCIT08), IEEE CS, Nov. 11~13, 2008, Novotel Ambassador Busan, Busan, Korea, pp. 248-253.

45.  Sotiris Kotsiantis, Local Random Subspace Method for Constructing Multiple Decision Stumps, 2009 IEEE International Conference on Information and Financial Engineering (ICIFE 2009), pp. 125-129.

46.  Sotiris Kotsiantis, Dimitris Kanellopoulos, Vasiliki Karioti and Vasilis Tampakas, On Implementing an Ontology-based Portal for Intelligent Bankruptcy Prediction, 2009 IEEE International Conference on Information and Financial Engineering (ICIFE 2009), pp. 108-112.

47.  Sotiris Kotsiantis, Dimitris Kanellopoulos, Vasiliki Karioti and Vasilis Tampakas, An ontology-based portal for credit risk analysis, IEEE ICCSIT 2009: Special Session on Artificial Intelligence and Neural Networks (ICAINN 2009), pp. 165-169.

48.  Sotiris Kotsiantis, P. E. Pintelas, Local Rotation Forest of Decision Stumps for Regression Problems, IEEE ICCSIT 2009: Special Session on Artificial Intelligence and Neural Networks (ICAINN 2009), pp. 170-174.

49.  T. Mouratis, Sotiris Kotsiantis, Increasing the Accuracy of Discriminative of Multinomial Bayesian Classifier in Text Classification, International Conference on Convergence and hybrid Information Technology (ICCIT09), IEEE CS, Nov. 24-26, 2009, pp. 1246-1251.

50.  Sotiris Kotsiantis, V. Tampakas, Increasing the Accuracy of Hidden Naive Bayes Model, 2nd International Conference on Data Mining and Intelligent Information Technology Applications, IEEE CS, 2010, Seoul, Korea, pp. 247-252.

51.  S. Kotsiantis, I. Tsagaraki, Ensemble of Classifiers for Handling Biomedical Problems, 2nd International Conference on Data Mining and Intelligent Information Technology Applications, IEEE CS, 2010, Seoul, Korea, pp. 404-409.

52.  Despina Deligianni and Sotiris Kotsiantis, Forecasting Corporate Bankruptcy with an Ensemble of Classifiers, Lecture Notes in Computer Science, 2012, Volume 7297, Pages 65-72

53.  Elias Zouboulidis and Sotiris Kotsiantis, Forecasting Fraudulent Financial Statements with Committee of Cost-Sensitive Decision Tree Classifiers, Lecture Notes in Computer Science, 2012, Volume 7297/2012, 57-64.

54.  Elias Kamos, Foteini Matthaiou and Sotiris Kotsiantis, Credit Rating Using a Hybrid Voting Ensemble, Lecture Notes in Computer Science, 2012, Volume 7297/2012, 165-173

55. E. Pappas, S. Kotsiantis, Integrating Global and Local Application of Discriminative Multinomial Bayesian Classifier for Text Classification, 1st International Symposium on Intelligent Informatics (ISI'12), 4 – 5 August 2012, Chennai, India, Advances in Intelligent Systems and Computing, Volume 182, Intelligent Informatics, Pages 49-55.

56.      Lipitakis Anastasia-Dimitra, Kotsiantis Sotiris, A hybrid Machine Learning methodology for imbalanced datasets, The 5th International Conference onInformation, Intelligence, Systems and Applications, IISA 2014, Greece, pp. 252 � 257

57.      Anastasia-Dimitra Lipitakis and Sotiris Kotsiantis, Combining ensembles algorithms of symbolic learners, 6th International Conference on Information, Intelligence, Systems and Applications (IISA2015), Ionian University, Corfu, Greece, July 6-8, 2015

58.      Anastasia-Dimitra Lipitakis, Gerasimos S. Antzoulatos, Sotiris Kotsiantis and Michael N. Vrahatis, Integrating Global and Local Boosting, 6th International Conference on Information, Intelligence, Systems and Applications (IISA2015), Ionian University, Corfu, Greece, July 6-8, 2015

59.      Nikos Fazakis, Stamatis Karlos, Sotiris Kotsiantis, Kyriakos Sgarbas, Speaker Identification using Semi-Supervised Learning, 17th International Conference on Speach and Computer SPECOM 2015, 20-24 September, Athens, Greece, Lecture Notes in Computer Science, Volume 9319, pp. 389-396

60.      Stamatis Karlos, Nikos Fazakis, Sotiris Kotsiantis, Kyriakos Sgarbas, Self-train Logitboost for Semi-supervised Learning, Engineering Applications of Neural Networks (EANN 2015). Island of Rhodes, Greece, 25-28 September 2015

61.      G. Kostopoulos, S. Kotsiantis, P. Pintelas, Predicting Student Performance in Distance Higher Education Using Semi-Supervised Techniques, MEDI 2015, Island of Rhodes, Greece, 25-28 September 2015

62.      Georgios Kostopoulos, Sotiris Kotsiantis and Panagiotis Pintelas, ESTIMATING STUDENT DROPOUT in DISTANCE HIGHER EDUCATION USING SEMI-SUPERVISED TECHNIQUES, PCI 2015, ACM

63 Christos Aridas and Sotiris Kotsiantis, Combining Random Forest and Support Vector Machine for semi-supervised learning, PCI 2015, ACM

64 Stamatis Karlos, Nikos Fazakis, Katerina Karanikola, Sotiris Kotsiantis and Kyriakos Sgarbas, Speech Recognition combining MFCCs and Image Features, 18th International Conference on Speach and Computer SPECOM 2016, LNCS

65 Stamatis Karlos, Nikos Fazakis, Sotiris Kotsiantis and Kyriakos Sgarbas, Effectiveness of semi-supervised learning in bankruptcy prediction, 7th International Conference on Information, Intelligence, Systems and Applications (IISA2016), IEEE

66 Nikos Fazakis, Stamatis Karlos, Sotiris Kotsiantis and Kyriakos Sgarbas, Self-Labeled Hidden Naive Bayes Algorithm for Semi-Supervised Classification, 7th International Conference on Information, Intelligence, Systems and Applications (IISA2016), IEEE (best student paper award)

67 Christos Aridas, Sotiris Kotsiantis and Michael Vrahatis, Increasing diversity in Random Forests using Naive Bayes, 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI 2016), Springer

68 Christos Aridas, Sotiris Kotsiantis and Michael Vrahatis, Combining Prototype Selection with Local Boosting, 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI 2016), Springer

69. Stamatis Karlos, Nikos Fazakis, Sotiris Kotsiantis and Kyriakos Sgarbas, Semi-supervised forecasting of fraudulent financial statements, PCI 2016, ACM (best paper award)

70. Vangjel Kazllarof, Stamatis Karlos, Angeliki-Panagiota Panagopoulou and Sotiris Kotsiantis, Automated hand gesture recognition for educational applications, PCI 2016, ACM   

71. S Karlos, G Kostopoulos, S Kotsiantis, V Tampakas, Using Active Learning Methods for Predicting Fraudulent Financial Statements, International Conference on Engineering Applications of Neural Networks, 351-362, Springer, 2017    

72. G Kostopoulos, AD Lipitakis, S Kotsiantis, G Gravvanis, Predicting Student Performance in Distance Higher Education Using Active Learning, International Conference on Engineering Applications of Neural Networks, 75-86, Springer, 2017    

73. CK Aridas, SAN Alexandropoulos, SB Kotsiantis, MN Vrahatis, Random Resampling in the One-Versus-All Strategy for Handling Multi-class Problems, International Conference on Engineering Applications of Neural Networks, 111-121, Springer, 2017    

 

 

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