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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 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 6 9. 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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