Publications

Book Chapters:

1. l Schetinin V., et al. Estimating Classification Uncertainty of Bayesian Decision Tree Technique on Financial Data. In: Perceptual Data Mining and Decision Making in Economics and Finance. Studies in Computational Intelligence. Springer, 155-179, 2007.
2. l Schetinin V., Zharkova V. A Bayesian Methodology of Averaging over Decision Trees for Solar Data Classification. In: Artificial Intelligence in Recognition and Classification of Astrophysical and Medical Images. Springer, 169-199, 2007.
3. l Schetinin V., et al. A Bayesian Methodology for Estimating Uncertainty of Decisions in Safety-Critical Systems. In: Integrated Intelligent Systems for Engineering Design. Frontiers in Artificial Intelligence and Applications. IOS Press, 82-96, 2006.
4. l Schetinin V., Schult J., Brazhnikov A. Neural-Network Technique for Visual Data Mining Clinical Electroencephalograms. In: Visual and Spatial Decision Making and Problem Solving. Springer, 335-370, 2005.

Journal Papers (ISI impact-factor):

1. l Uglov J., Jakaite L., Schetinin V., Maple C. Comparing Robustness of Pairwise and Multiclass Neural-Network Systems for Face Recognition. EURASIP Journal of Applied Signal Processing, Hindawi, USA, 2008. (ISI 1.05)
2. l Schetinin V., et al. Confident Interpretation of Bayesian Decision Trees for Clinical Applications. IEEE Transaction on IT in Biomedicine, 11:3, 312-319, 2007. (ISI 1.54)
3. l Krzanowski W., Fieldsend J.E., Bailey T.C., Everson R.M., Partridge D., Schetinin V. Confidence in Classification: a Bayesian Approach. Journal of Classification, Springer, 23:2, 199-220, 2006. (ISI 0.6)
4. l Bailey T.C., Everson R.M., Fieldsend J.E., Krzanowski W.J., Partridge D., Schetinin V. Representing Classifier Confidence in the Safety Critical Domain - an Illustration from Mortality Prediction in Trauma Cases. Neural Computing & Applications, Springer, 16:1, 1-10, 2006. (ISI 0.61)
5. l Schetinin V., et al. Comparison of the Bayesian and Randomised Decision Tree Ensembles within an Uncertainty Envelope Technique. Journal of Mathematical Modelling and Algorithms, Springer, 5:4, 397-416, 2006.
6. l Zharkova V.V., Schetinin V. Filament Recognition in Solar Images with the Artificial Neural Network. Solar Physics, Springer, 228:1-2, 137-148, 2005. (ISI 2.6)
7. l Schetinin V., Schult J. Learning Polynomial Networks for Classification of Clinical Electroencephalograms. Soft Computing, Springer, 10: 4, 397-403, 2005. (ISI 0.52)
8. l Schetinin V., Abrukov V., Brazhnikov A. Self-Organising Neural-Network Models for Mining Underrepresented Data. Automatic Control and Computer Science, Allerton Press, 39:2, 15-23, 2005.
9. l Schetinin V., Schult J. A Neural-Network Technique for Learning Concepts from Electroencephalograms. Theory in Biosciences, Elsevier, 124, 41-53, 2005. (ISI 0.92)
10. l Schetinin V., Schult J. The Combined Technique for Detection of Artefacts in Clinical Electroencephalograms of Sleeping Newborns. IEEE Transaction on Information Technologies in Biomedicine, 8:1, 28-35, 2004. (ISI 1.54)
11. l Schetinin V. A Learning Algorithm for Evolving Cascade Neural Networks. Neural Processing Letters, Kluwer, 17: 1, 21-31, 2003. (ISI 0.63)
12. l Schetinin V. Pattern Recognition with Neural Network. Optoelectronics, Instrumentation and Data Processing, Allerton Press, 2, 75-80, 2000.
13. l Schetinin V., Brazhnikov A. Extracting Decision Rules Using Neural Networks. Biomedical Engineering, Kluwer, 1, 16-21, 2000.
14. l Schetinin V., Solomakha A. Prediction of Postoperative Complications with Neural Networks. Biomedical Engineering, Kluwer, 2, 21-24, 2000.

Conference Papers:

1. l Jakaite L., Schetinin V., Maple C. Feature Importance in Bayesian Assessment of Newborn Brain. Proceedings of the 9th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (AIKED), University of Cambridge, UK, 2010, edited by L. A. Zadeh et al, WSEAS Press, pp 191 – 195, 2010. http://arxiv.org/abs/1002.4522
2. l Schetinin V., Li, D., Maple C. An Evolutionary-Based Approach to Learning Multiple Decision Models from Underrepresented Data. The 4th International Conference on Natural Computation (ICNC'08), IEEE Computer Society, 2008. http://arxiv.org/abs/0805.3800
3. l Jakaite L., Schetinin V. Feature Selection for Bayesian Evaluation of Trauma Death Risk, 14th Nordic Baltic Conference on Biomedical Engineering and Medical Physics, 2008.
4. l Schetinin V., Maple C. A Bayesian Model Averaging Methodology for Detecting EEG Artefacts. 15th International Conference on Digital Signal Processing, DSP-2007, Sponsored by the IEEE, Cardiff, 499-502, 2007.
5. l Schetinin V, Krzanowski W.J, Maple C. The Bayesian Decision Tree Technique Using an Adaptive Sampling Scheme. The 20th IEEE International Symposium on Computer-Based Medical Systems, CBMS-2007, Maribor, 121-126, 2007.
6. l Maple C., Schetinin V. A Bayesian Model Averaging Methodology for Estimating Reliability of Decisions in Multimodal Biometrics. IEEE Conference on Data Warehousing and Mining Applications, DAWAM-2006, Vienna. IEEE Computer Society, 929-935, 2006.
7. l Schetinin V., Zharkova V., Zharkov S. Bayesian Decision Tree Averaging for the Probabilistic Interpretation of Solar Flare Occurrences. B. Gabrys, R.J. Howlett, and L.C. Jain (Eds.), Lecture Notes in Artificial Intelligence, 4253, Springer, 523-532, 2006.
8. l Partridge D., Schetinin V., Li D., Coats T., Fieldsend J.E., Krzanowski W.J., Everson R.M., Bailey T.C. Interpretability of Bayesian Decision Trees Induced from Trauma Data. Lecture Notes in Artificial Intelligence, 4029, Springer, 972-981, 2006.
9. l Partridge D., Bailey T., Everson , Fieldsend J., Hernandez A., Krzanowski W., Schetinin V. Classification with Confidence for Critical Systems. Developments in Risk-based Approaches to Safety. Proceedings of the 14 Safety-Critical Systems Symposium, Bristol, UK, 231-240. 2006.
10. l Schetinin V., et al. The Bayesian Decision Tree Technique with a Sweeping Strategy. IEEE Conference on Advances in Intelligent Systems - Theory and Applications. Luxembourg, 2004. http://arxiv.org/abs/cs/0504042
11. l Schetinin V., et al. Experimental Comparison of Classification Uncertainty for Randomised and Bayesian Decision Tree Ensembles. Intelligent Data Engineering and Automated Learning. Lecture Notes in Computer Science, Springer, 3177, 726-732, 2004. http://arxiv.org/abs/cs/0504043
12. l Schetinin V., Schult J., Scheidt B., Kuriakin V. Learning Multiclass Neural-Network Models from Electroencephalograms. Knowledge-Based Intelligent Information and Engineering Systems. Lecture Notes in Computer Science, 2773, Springer, 155-162, 2003.
13. l Zharkova V.V., Schetinin V. A Neural-Network Technique for Recognition of Filaments in Solar Images. Lecture Notes in Computer Science, 2773, Springer, 148-154, 2003.
14. l Abrukov V., Schetinin V., Deltsov P. Using Artificial Neural Networks for Combustion Interferometry. Lecture Notes in Computer Science, 2773, Springer, 684-690, 2003.
15. l Fieldsend J., Bailey T.C., Everson R.M., Krzanowski W.J., Partridge D., Schetinin V. Bayesian Inductively Learned Modules for Safety Critical Systems. Proceedings of the 35th Symposium on the Interface: Computing Science and Statistics. Salt Lake City, 110-125, 2003.
16. l Zharkova V.V., Schetinin V. Recognition of Filaments in Solar Images with an Artificial Neural Network, ESANN-2004, Bruges, 521-526, 2003.
17. l Schetinin V. A Neural-Network Decision Tree for Learning Concepts from EEG Data. NIMIA-SC2001 NATO Advanced Study Institute on Neural Networks, IEEE Sponsored, Italy, 147-154, 2001.
18. l Schetinin V. Polynomial Neural Networks for Classifying EEG Signals. NIMIA-SC2001, IEEE Sponsored, Italy, 155-162, 2001.