Representative
Publications ORCID
Google
Scholar and Research Gate
(see also http://www.cwu.edu/~Imaglab/pages/pubs.html
for Imaging Analysis),
|
Kovalerchuk B, Nazemi K, Andonie R,
Datia N, Banissi E, (Eds).
Artificial Intelligence
and Visualization: Advancing Visual Knowledge Discovery, Springer, April
2024 |
|
Kovalerchuk
B, Nazemi K, Andonie R, Datia N, Banissi E, (Eds). Integrating Artificial Intelligence and Visualization
for Visual Knowledge Discovery Springer,
2022 |
Visual Knowledge Discovery and Machine Learning Springer Nature, 2018 |
|
|
|
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Dedicated to Professor Boris
Kovalerchuk on his Anniversary Editor: Kreinovich, V. Springer,
2017 |
|
Kovalerchuk, B, Schwing, J. (eds) Visual and Spatial Analysis: Advances on Data Mining,
Reasoning and Problem Solving, Springer, 2004 Ch 1: B. Kovalerchuk, Decision process and
its visual aspects
|
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Kovalerchuk, B., Vityaev,
E., Data Mining in Finance: Advances in Relational and Hybrid Methods,
|
Computational
Intelligence (CI 2009), IASTED Int. Conf. Editor: B. Kovalerchuk, August 17-19,
2009, Honolulu, Hawaii, USA, Acta Press.
Computational
Intelligence 2006, IASTED Int. Conf. Editor: B. Kovalerchuk, San Francisco,
California, USA, Publication Date: 20-Nov-2006, Hardcopy ISBN:
0-88986-602-3; CD ISBN: 0-88986-603-1, Acta Press
2023
1. Recaido
C., Kovalerchuk B., Visual
Explainable Machine Learning for High-Stakes Decision-Making with Worst Case
Estimates. In: Data Analysis and
Optimization, in Honor of Boris Mirkin’s 80th
Birthday, Eds. B. Goldengorin, S.
Kuznetsov, pp. 293-331, Springer, 2023.
2. Kovalerchuk, B. Explainable Machine Learning and
Visual Knowledge Discovery,.
In: Rokach, L., Maimon, O., Shmueli, E. (eds) Machine Learning for Data Science
Handbook. Springer, pp. 913-943, Cham, 2023, https://doi.org/10.1007/978-3-031-24628-9_39
3. Kovalerchuk, B., Vityaev, E., Demin, A., Wilinski, A. Interpretable Machine Learning for
Financial Applications. In:
Rokach, L., Maimon, O., Shmueli, E. (eds) Machine Learning for Data Science
Handbook. Springer, Cham, pp. 721-749 2023, https://doi.org/10.1007/978-3-031-24628-9_32
2022
4. Kovalerchuk B, Andonie R, Datia N, Nazemi K, Banissi E. Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions. In: Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery 2022 (pp. 1-27). Springer, Cham.
5. Kovalerchuk B, Kalla DC, Agarwal B. Deep Learning Image Recognition for Non-images. In: Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery 2022 (pp. 63-100). Springer, Cham.
6. Wagle SN, Kovalerchuk B. Self-service Data Classification Using Interactive Visualization and Interpretable Machine Learning. In: Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery 2022 (pp. 101-139). Springer, Cham.
7. McDonald R, Kovalerchuk B. Non-linear Visual Knowledge Discovery with Elliptic Paired Coordinates. In: Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery 2022 (pp. 141-172). Springer, Cham.
2021
8. Kovalerchuk, B., Ahmad, M.A., Teredesai A., Survey of Explainable Machine Learning with Visual and Granular Methods beyond Quasi-explanations, In: Interpretable Artificial Intelligence: A Perspective of Granular Computing, W. Pedrycz, S.M.Chen ((Eds.), Springer, 2021, 45 pp.
2020
9. Kovalerchuk B. Enhancement of Cross Validation Using Hybrid Visual and Analytical Means with Shannon Function. In: Beyond Traditional Probabilistic Data Processing Techniques: Interval, Fuzzy etc. Methods and Their Applications 2020 (pp. 517-543). Springer
10. Kovalerchuk, B., Relationships Between Probability and Possibility Theories, In: Uncertainty Modeling, Studies in Computational Intelligence 683, Kreinovich, V.(Ed.), Springer, 2017, 97-122, DOI 10.1007/978-3-319-51052-1_7
11. Resconi G., Kovalerchuk, B., Copula as a Bridge Between Probability Theory and Fuzzy Logic, In: Uncertainty Modeling, Kreinovich, V.(Ed.), Springer, 2017, 251-272.
12. Vityaev E., Kovalerchuk, B. Ontological Data Mining, In: Uncertainty Modeling, In: Uncertainty Modeling, Kreinovich, V.(Ed.), Springer, 2017, 277-292.
2016
13. Kovalerchuk B., Perlovsky L., Sensor Resource Management: Intelligent Multi-objective Modularized Optimization Methodology and Models, In: R. Abielmona et al. (eds.), Recent Advances in Computational Intelligence in Defense and Security, Studies in Computational Intelligence 621, pp. 695-726, Springer, Switzerland, 2016, DOI 10.1007/978-3-319-26450-9_25
2014
14. Galitsky B., Kovalerchuk B., Improving Web Search Relevance with Learning Structure of Domain Concepts, In: Clusters, Orders, and Trees: Methods and Applications, Aleskerov, F. Goldengorin, B. Pardalos, P.M. (Eds.), pp. 311-363, Springer, 2014.
2013
15. Kovalerchuk B., Quest for Rigorous Combining Probabilistic and Fuzzy Logic Approaches for Computing with Words, In: R. Seising, E. Trillas, C.Moraga, S.Termini (eds.): On Fuzziness. A Homage to Lotfi A. Zadeh (Studies in Fuzziness and Soft Computing Vol. 216), Berlin, New York: Springer 2013. Vol. 1. pp. 333-344.
2012
16. Kovalerchuk, B., Delizy, F., Riggs, L., Visual Data Mining and Discovery with Binarized Vectors, in: Data Mining: Foundations and Intelligent Paradigms (2012) 24: 135-156
2011
17. Kovalerchuk, B., Balinsky, A., Visual Data Mining and Discovery in Multivariate Data using Monotone n-D Structure, In: Knowledge Processing and Data Analysis, Wolff, K.E.; Palchunov, D.E.; Zagoruiko, N.G.; Andelfinger, U. (Eds.), Springer, Lecture Notes in Computer Science, 2011, Volume 6581/2011, pp. 297-313, DOI: 10.1007/978-3-642-22140-8_20
2010
18. Resconi, G., Kovalerchuk, B., Agents in Quantum and Neural Uncertainty In: Multi-Agent Applications for Evolutionary Computation and Biologically Inspired Technologies. Shu-Heng Chen, Yasushi Kambayashi (Eds), 2010, IGI Global, Hershey, New York, ISBN: 978-1-60566-898-7, pp 50-76.
19. Kovalerchuk, B., Vityaev, E., Data Mining for Financial Applications. In: O. Maimon, L. Rokach (Eds.): The Data Mining and Knowledge Discovery Handbook. Springer 2005, pp. 1203-1224. Second edition 2010
2009
20. Resconi, G., Kovalerchuk, B., Agent Uncertainty Model and Quantum Mechanics Representation: Non-locality Modeling, In: Jain L.C., Nguyen N.T. (Eds.): Knowledge Processing and Decision Making in Agent-Based Systems, Springer, 2009, pp 217-246.
2001
21. Kovalerchuk, B., Vityaev E., Ruiz J.F., Consistent and Complete Data and "Expert" Mining in Medicine, In: Medical Data Mining and Knowledge Discovery, Springer, 2001, pp. 238-280.
1997
22. Triantaphyllou, E., Kovalerchuk, B., Deshpande, A., Some recent developments of using logical analysis for inferring a Boolean function with few clauses. In: R. Barr, R. Helgason, L. Kennington (Eds.), Interfaces in Computer Science and Operations Research Series, Vol. 7, Kluwer, 1997, pp. 215-236
1994
23. Kovalerchuk, B. Current situation in foundations of fuzzy optimization. In: M. Delgado, J. Kacprzyk, J.‑L. Verdegay and M.A. Vila (Eds.): Fuzzy Optimization: Recent Advances, Studies in Fuzziness, Springer, Heidelberg, NY, 1994, pp.45‑60.
1996
24. Kovalerchuk, B., Triantaphyllou, E., Ruiz, J.F. Monotonicity and logical analysis of data: a mechanism for evaluation of mammographic and clinical data. In: Computer applications to assist radiology, Symposia Foundation, Carlsbad, CA, 1996, pp. 191-196.
1980
25. Kovalerchuk, B. Optimal distribution of n jobs to m partially changeable processors. In: Application of optimization methods in planning and control. (Ed. L.V. Kantorovich), Moscow, 1980, pp. 245‑253 (in Russian).
26. Vityaev,
E., Kovalerchuk, B., Website Scientific Discovery
27. Kovalerchuk, B., Vityaev, E., Yusupov, H. Symbolic Methodology in Numeric Data Mining: Relational Techniques for Financial Applications. arXiv.org: Computational Engineering, Finance, and Science, cs.CE/0208022, 20 p., 2002
28. Kovalerchuk, B., Comments on proposed Microsoft OLE DB for Data Mining standard, KDNuggets, 2000
29. Kovalerchuk, B.,Vityaev, E., Ruiz, J., Integrated consistent and complete expert and data mining, 2003
2024
30. Boris
Kovalerchuk. Interpretable AI/ML for High-stakes Tasks with Human-in-the-loop:
Critical Review and Future Trends, 29 February 2024, PREPRINT (Version 1)
available at Research Square, https://doi.org/10.21203/rs.3.rs-3989807/v1 2023
2023
31. Huber L., Kovalerchuk B., No-Code Platform for Visual Knowledge Discovering in
General Line Coordinates: DV 2.0, in: 27th International Conference Information
Visualisation, Tampere, Finland, IEEE, 2023. pp. 316-322
32. Martinez J., Kovalerchuk B., General Line Coordinates in 3D, In 27th International Conference Information Visualisation, Tampere, Finland, IEEE, 2023, pp 308-315, http://arxiv.org/abs/2403.13014
33. Kovalerchuk, B., Fegley B., Principal
Components in General Line Coordinates for Visualization and Machine Learning. 27th
International Conference Information Visualisation,
Tampere, Finland, IEEE, 2023, pp.
300-307.
34. Cutlip N., Kovalerchuk B., Lossless Interpretable Glyphs for Visual Knowledge
Discovery in High-Dimensional Data, 27th
International Conference Information Visualisation,
Tampere, Finland, IEEE, 2023, pp.
292-299.
2022
35. Kovalerchuk. B., Lossless
High-Dimensional Visualization for Scientific Discovery, DoE ASCR Workshop
on Visualization for Scientific Discovery, Decision-Making, &
Communication. January 18-20, 2022
36.
Kovalerchuk
B., McCoy E., Explainable Mixed Data Representation and
Lossless Visualization Toolkit for Knowledge Discovery, in: 26th
International Conference Information Visualisation,
2022, pp. 314 – 321, IEEE, arXiv:2206.06476. 37. Worland A.,
Wagle S., Kovalerchuk B., Visualization of Decision Trees based on
General Line Coordinates to Support Explainable Models, in: 26th International Conference
Information Visualisation, 2022, pp. 351–358, IEEE, arXiv:2205.04035. 38. Recaido C., Kovalerchuk B., Interpretable
Machine Learning for Self-Service High-Risk Decision-Making, in: 26th International Conference
Information Visualisation, 2022, pp. 322–329, IEEE, arXiv:2205.04032. |
2021
39. Kovalerchuk B, Hayes D. Discovering Interpretable
Machine Learning Models in Parallel Coordinates. In: 2021 25th International
Conference Information Visualisation (IV) 2021 Jul 5
(pp. 181-188). IEEE, https://arxiv.org/pdf/2106.07474
40. Kovalerchuk, B., Phan J., Full interpretable machine
learning in 2D with inline coordinates. 25th International Conference
Information Visualisation IV-2021, Australia, Jul 5-9 2021, Vol. 1, pp. 189-196, IEEE, DOI
10.1109/IV53921.2021.00038, https://arxiv.org/pdf/2106.07568
41. Kovalerchuk B., Germano Resconi (1942-2020) and
research on uncertainty modeling, Mathware & Soft
Computing Magazine. Vol. 27 n. 1, pp. 10-14, 2021, https://www.eusflat.org/materials/mathware-vol27n1.pdf
42. SN
Wagle, B Kovalerchuk, Self-service
Data Classification Using Interactive Visualization and Interpretable Machine
Learning, arXiv preprint https://arxiv.org/pdf/2107.04971 |
2020
51. Kovalerchuk B., Intelligible Machine Learning and
Knowledge Discovery Boosted by Visual Means, WSDM '20: Proceedings of the 13th
International Conference on Web Search and Data Mining, January 2020, pp.
881–883, https://doi.org/10.1145/3336191.3371872,
52. Kovalerchuk,
B, Agarwal B., Kalla, D., Solving Non-image Learning Problems by Mapping to Images,
24th International Conference Information Visualisation
IV-2020, Melbourne, Victoria, Australia, 7-11 Sept.2020, IEEE, DOI
10.1109/IV51561.2020.00050.
53. Wagle, S., Kovalerchuk, B., Interactive Visual Self-Service Data Classification Approach to Democratize Machine Learning , 24th International Conference Information Visualisation IV-2020, Melbourne, Victoria, Australia, 7-11 Sept.2020, IEEE, DOI 10.1109/IV51561.2020.00052.
54. McDonald, R., Kovalerchuk, B. Lossless Visual Knowledge Discovery in High Dimensional Data with Elliptic Paired Coordinates, 24th International Conference Information Visualisation IV-2020, Melbourne, Victoria, Australia, 7-11 Sept.2020, IEEE, DOI 10.1109/IV51561.2020.00053,
2019
55. N. Neuhaus, B. Kovalerchuk, Interpretable
Machine Learning with Boosting by Boolean Algorithm, Joint 2019 8th Intern. Conf. on Informatics, Electronics & Vision
(ICIEV) & 3rd Intern. Conf. on Imaging, Vision & Pattern Recognition
(IVPR), Spokane, WA, 2019, 307-311.
56. Kovalerchuk B. Tutorial: Visual Knowledge Discovery
and Machine Learning. In: 2019 Joint 8th International Conference on
Informatics, Electronics & Vision (ICIEV) and 2019 3rd International
Conference on Imaging, Vision & Pattern Recognition (icIVPR)
2019 May 30 (pp. 1-2). IEEE.
57. Kovalerchuk B. Interpretable Knowledge Discovery
Reinforced by Visual Methods. In: Proceedings of the 25th ACM SIGKDD
International Conference on Knowledge Discovery & Data Mining 2019 Jul 25
(pp. 3219-3220). ACM.
2018
58. Kovalerchuk B., Neuhaus, N. Toward Efficient
Automation of Interpretable Machine Learning. In: 2018 IEEE International
Conference on Big Data, pp. 4933-4940, 978-1-5386-5035-6/18, Seattle, Dec.
10-13, 2018 IEEE.
59. Kovalerchuk, B., Grishin, V. Reversible Data
Visualization to Support Machine Learning, In: Human Interface and the
Management of Information. Interaction, Visualization, and Analytics, Lecture
Notes in Computer Science series, Vol. 10904, 2018, pp. 45-59, Springer.
60. Kovalerchuk B., Gharawi A., Decreasing
Occlusion and Increasing Explanation in Interactive Visual Knowledge Discovery,
In: Human Interface and the Management of Information. Interaction,
Visualization, and Analytics, Lecture Notes in Computer Science series, Vol.
10904, 2018, pp. 505-526. Springer.
61. Dovhalets, D., Kovalerchuk, B., Vajda, S. Andonie, R.,
Deep
Learning of 2-D Images Representing n-D Data in General Line Coordinates,
ISASE-MAICS 2018, Spokane, WA, 2018.
https://www.jstage.jst.go.jp/article/isase/ISASE2018/0/ISASE2018_1_18/_article/-char/en,
2017
62. Kovalerchuk,
B. & Dovhalets, D., 2017. Constructing Interactive Visual Classification,
Clustering and Dimension Reduction Models for n-D Data. Informatics, 4(3),
p.23. Available at: http://dx.doi.org/10.3390/informatics4030023.
63. Wilinski,
A., & Kovalerchuk, B. Visual
knowledge discovery and machine learning for investment strategy. Cognitive
Systems Research, 44, pp.100–114. Available at:
http://dx.doi.org/10.1016/j.cogsys.2017.04.004.
64. Kovalerchuk,
B., Kovalerchuk, M., Toward Virtual Data Scientist, In: Proc. of IJCNN 2017,
Anchorage, Alaska, May 14-19, 2017, IEEE, pp. 3073-3080.
65. Smigaj,
A. & Kovalerchuk, B., 2017. Visualizing Incongruity and Resolution: Visual
Data Mining Strategies for Modeling Sequential Humor Containing Shifts of
Interpretation. Lecture Notes in Computer Science, pp.660–674. Available at: http://dx.doi.org/10.1007/978-3-319-58697-7_49.
66. Kovalerchuk
B., Grishin V. Adjustable
General Line Coordinates for Visual Knowledge Discovery in n-D data, Information Visualization, First
published date: July-09-2017, doi: 10.1177/1473871617715860, Information
Visualization, Vol. 8 Issue 1, January 2019, p. 3–32.
67. Kovalerchuk, B., 2017. Visual cognitive algorithms for high-dimensional data and super-intelligence challenges. Cognitive Systems Research, 45, pp.95–108. Available at: http://dx.doi.org/10.1016/j.cogsys.2017.05.007.
2016
68. Kovalerchuk B., Kreinovich
V., Concepts
of solutions of uncertain equations with intervals, probabilities and fuzzy
sets for applied tasks, Granular Computing, 2016, DOI
10.1007/s41066-016-0031-4. pdf.
69. Kovalerchuk B., Grishin V.,
Visual Data Mining in Closed Contour Coordinates, IS&T International
Symposium on Electronic Imaging 2016, San Francisco, CA, Visualization and Data
Analysis 2016, VDA-503.1- VDA-503.10. pdf
70. Kovalerchuk, B., Kreinovich
V. Comparison of formulations of applied tasks with intervals, fuzzy sets and
probability approaches, 2016 IEEE International Conference on Fuzzy Systems
(FUZZ), 1478-1483, IEEE 2016, pdf.
71. Kovalerchuk B.,
Super-intelligence Challenges and Lossless Visual Representation of
High-Dimensional Data, 2016 International Joint Conference on Neural Networks
(IJCNN), 1803-1810, IEEE 2016, pdf
2015
72. Kovalerchuk, B., Perlovsky L., Rigorous Sensor Resource Management:
Methodology and Evolutionary Optimization,
In: Symposium on Computational Intelligence for Security and Defense
Applications (IEEE CISDA 2015, 05-26-28, 2015, Verona, NY), IEEE, pp.1-8, DOI:
10.1109/CISDA.2015.7208621
73. Kovalerchuk, B. Summation of
Linguistic Numbers, Proc. of North American Fuzzy Information Processing
Society (NAFIPS) and World Congress on Soft Computing, 08-17-19, 2015, Redmond,
WA pp.1-6. DOI: 10.1109/NAFIPS-WConSC.2015.7284161
74. Kovalerchuk, B., Smigaj A.,
Computing with words beyond quantitative words: incongruity modeling, Proc. of
North American Fuzzy Information Processing Society (NAFIPS) and World Congress
on Soft Computing, 08-17-19, 2015, Redmond, WA, IEEE, pp. 226-233. DOI:
10.1109/NAFIPS-WConSC.2015.7284193
2014
75. Kovalerchuk, B., Visualization of multidimensional data with collocated paired coordinates and general line coordinates, In: SPIE Visualization and Data Analysis 2014, Proc. SPIE 9017, Paper 90170I, doi: 10.1117/12.2042427, 15 p.
76. Kovalerchuk B., Grishin V., Collaborative lossless visualization of n-D data by collocated paired coordinates, CDVE 2014, Seattle, UW, Sept 2014, Y. Luo(Ed.): CDVE 2014, LNCS 8683, pp. 19–26,Springer Switzerland, 2014.
77. Grishin V., Kovalerchuk, B., Multidimensional collaborative lossless visualization: experimental study, CDVE 2014, Seattle, UW, Sept 2014. Luo (Ed.): CDVE 2014, LNCS 8683, pp. 27–35, Springer Switzerland, 2014.
78. Kovalerchuk, B., Probabilistic Solution of Zadeh’s test problems, In: A. Laurent et al. (Eds.): IPMU 2014, Part II, CCIS 443, pp. 536–545, Springer, 2014
2013
79. Vityaev,
E.E.,Perlovsky,L.I., Kovalerchuk B,Ya.,
Speransky S.O., Probabilistic dynamic logic of cognition,
Biologically
Inspired Cognitive Architectures Journal, Vol. 6, 2013, pp.
159-168, Elsevier, 2013, Invited Article
80. Kovalerchuk, B., Quest for rigorous intelligent tutoring systems under uncertainty: Computing with Words and Images, In: Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013. pp. 685 - 690, DOI: 10.1109/IFSA-NAFIPS.2013.6608483
81. Kovalerchuk, B., Correlation of partial frames in video matching, SPIE Proceedings Vol. 8747, Geospatial InfoFusion III, Matthew F. Pellechia; Richard J. Sorensen; Kannappan Palaniappan, Editors, 87470l, pp. 1-12, 2013, Doi: 10.1117/12.2016645
82. Kovalerchuk B., Streltsov S., Best M., Guidance in feature extraction to resolve uncertainty, SPIE Proceedings Vol. 8747, Geospatial InfoFusion III, Matthew F. Pellechia; Richard J. Sorensen; Kannappan Palaniappan, Editors, 12 pages; 874707, 2013, DOI: 10.1117/12.2016509
2012
83. B.Kovalerchuk, Overview of the Panel at WCCI-2012, Mathware & Soft Computing Magazine, Vol. 19 n. 2, pp.
28-30, 2012.
84. B. Kovalerchuk, On Rigorous Computing with Words, Mathware & Soft Computing Magazine, Vol. 19 n. 2, pp. 38-40, 2012.
85. B. Kovalerchuk, L. Perlovsky, G.Wheeler, Modeling of Phenomena and Dynamic Logic of Phenomena, Journal of Applied Non-classical Logics, 22(1): 51-82 (2012). http://www.tandfonline.com/doi/abs/10.1080/11663081.2012.682439
86. B.Kovalerchuk, L.Perlovsky, M.Kovalerchuk, Modeling spatial uncertainties in geospatial data fusion and mining, SPIE Proc., 8396-24, pp1-10 (2012).
87. B.Kovalerchuk Fuzzy Logic, Probability, and Measurement: Similarities and Differences in Computing with Words, 2012 IEEE World Congress on Computational Intelligence, Brisbane, Australia. IEEE digital media 2012.
2011
88. Kovalerchuk B. Perlovsky L.
Uncertainty Modeling for Spatial Data Fusion and Mining, In: Proc. of
2011 IEEE Symposium Series on Computational Intelligence (SSCI), Paris, France,
11 Apr - 15 Apr 2011
89. B. Galitsky, J. Lluis de la Rosa, B. Kovalerchuk: Discovering common outcomes of agents' communicative actions in various domains. Knowl.-Based Syst. 24(2): 210-229 (2011)
90. B.Galitsky, B.Kovalerchuk, J. Lluis de la Rosa: Assessing plausibility of explanation and meta-explanation in inter-human conflicts. Eng. Appl. of AI 24(8): 1472-1486 (2011)
91. Витяев Е.Е., Перловский Л.И., Ковалерчук Б.Я., Сперанский С.О. Вероятностная динамическая логика мышления, Нейроинформатика, 2011, том 5, № 1 (in Russian)
2010
92. Kovalerchuk, B., Delizy F., Riggs L., Vityaev E., Visual
Discovery in Multivariate Binary Data, SPIE Vol. 7530: SPIE Visualization and
Data Analysis 2010, Jinah Park; Ming C. Hao; Pak C. Wong; Chaomei
Chen, Editors, 75300B
93. Kovalerchuk B., Resconi., G. Agent-based Uncertainty Logic Network, 2010 IEEE World Congress on Computational Intelligence, Barcelona, Jul 18-23, 2010.
94. Kovalerchuk B., Interpretable Fuzzy Systems: Analysis of T-norm interpretability, 2010 IEEE World Congress on Computational Intelligence, Barcelona, Jul 18-23, 2010).
95. Vityaev, E., Kovalerchuk B., Perlovsky L., Smerdov S., Probabilistic Dynamic Logic of Phenomena and Cognition, 2010 IEEE World Congress on Computational Intelligence, Barcelona, Jul 18-23, 2010.
2009
96. Resconi,
G., Kovalerchuk, B., Agents`
model of uncertainty, Knowledge and Information Systems Journal, Springer
London, vol. 18, no. 2, pp. 213-229, Feb 2009, DOI 10.1007/s10115-008-0164-0.
97. Galitsky B, Kovalerchuk B., Characterizing dialogue plausibility by explanation phase space, In Computational Intelligence (CI 2009), IASTED Int. Conf. Editor: B. Kovalerchuk, August 17-19, 2009, Honolulu, Hawaii, USA, Acta Press.
98. Kovalerchuk, B., Modeling ATR processes prior to experimentation to predict ATR system performance, The 12th International Conference on Information Fusion, Seattle, USA, 6-9 July 2009
99. Kovalerchuk B., Perlovsky L., Fusion and Mining Spatial Data in Cyber-physical space with Phenomena Dynamic Logic, In: Proceedings of the 2009 International Joint Conference on Neural Networks Atlanta, Georgia, USA, pp: 2440-2447
100. Resconi G., Kovalerchuk, B., Agents in Neural Uncertainty, In: Proc. IEEE 2009 International Joint Conference on Neural Networks, Atlanta, GA, June 2009, pp: 2448-2455 http://doi.ieeecomputersociety.org/10.1109/IJCNN.2009.5178930
101. Vityaev E., Kovalerchuk, B., Method for extracting knowledge from experts, In: Information Technology in Social Sciences, Vol. 13, IAET SO RAN, Novosibirsk, Russia, 2009, pp. 75-81 (in Russian)
2008
102.
Kovalerchuk, B., Vityaev E., Symbolic Methodology for
numeric data mining, Intelligent Data Analysis, vol. 12, N 2, 2008, pp.
165-188.
103. Vityaev E., Kovalerchuk, B., Relational methodology for data mining and knowledge discovery, Intelligent Data Analysis, vol. 12, N 2, 2008, pp. 189-210.
104. Kovalerchuk B., Combining Invariance, Robustness, and Stability in Computer Vision, In: Proceedings of the Tenth IASTED International Conference on Computer Graphics and Imaging, Feb.13-15, 2008, Innsbruck, Austria, 2008 ACTA Press, Anaheim, CA, 600-803, pp. 267-272.
105. Kovalerchuk B., Perlovsky, L., Dynamic Logic of Phenomena and Cognition, In: The 2008 IEEE World Congress on Computational Intelligence (WCCI 2008), Hong Kong, IEEE 2008, 3529-3536.
106. Resconi, G., Kovalerchuk B., Fusion in Agent-Based Uncertainty Theory and Neural Image of Uncertainty, In: The 2008 IEEE World Congress on Computational Intelligence (WCCI 2008), Hong Kong, IEEE 2008, 3537-3543.
107. Galitsky, B., Kuznetsov, S., Kovalerchuk, B., Argumentation vs Meta-argumentation for the Assessment of Multi-agent Conflict, The Twenty-Third AAAI Conference On Artificial Intelligence (AAAI-08), July 13-17, 2008, Chicago
108. Galitsky B., Kuznetsov S.O., Kovalerchuk B. Argumentation Phase Space of a Multi-agent System. XIth Russian Artificial Intelligence Conference, Dubna, Russia, Sept 29-Oct 3, 2008.
109. Vityaev E., Kovalerchuk B., Fedotov, A., Barakhnin B., Below S., Durdin D., Demin A., Discovery regularities and recognition of anomalous events in the computer network traffic data. Vestnik Novosibisk State University, Information Technology, V. 6 (2), Novosibisk, Russia, 2008, pp. 57-68 (in Russian).
2007
110. Zagoruiko, N. G., Gulyaevskii, S. E., Kovalerchuk,B.Ya. Ontology of the Data Mining Subject Domain, Pattern Recognition and Image Analysis Journal, 2007, Vol. 17, No. 3, pp. 349-356. Pleiades Publishing, Ltd.
111. Kovalerchuk B., Visual Knowledge Discovery: from Euclid and Diophantus to Visual Data Mining, In; Proc. Knowledge-Ontologies-Theory Int. Conference, Russian Academy of Sciences, Novosibirsk, Russia, v. pp. 71-72 (in Russian).
112. Doucette, P., Kovalerchuk, B., Brigantic, R., Seedahmed, G., Graff, B., Methodology for automated image-to-vector registration, Proc. IEEE Applied Imagery Pattern Recognition (AIPR) workshop 2007, Washington, DC, Oct 10-12, 2007
113. Kovalerchuk, B., Vityaev E., and Holtfreter, R., Correlation of Complex Evidence in Forensic Accounting Using Data Mining, Journal of Forensic Accounting, 1524-5586 / Vol.VIII (2007), pp. 53-88, 2007, R.T. Edwards, Inc.
114. Resconi G., Kovalerchuk, B., Hierarchy of logics of irrational and conflicting agents, In Nguyen N.T. et al. (2007, Eds): Agent and Multi-agent Systems: Technologies and Applications. Proceedings of KES-AMSTA 2007, Wroclaw, Poland, May 1-6, 2007, Lecture Notes in Artificial Intelligence 4496. Springer, pp. 179-189.
115. Kovalerchuk, B., Perlovsky, L. Modeling Field Theory and Dynamic Logic, In: Proc of IEEE International Conference Integration of Knowledge Intensive Multi-Agent Systems, KIMAS '07, April 29-May 3, 2007, Waltham, Massachusetts
116. Kovalerchuk, B., Resconi G., Break of Logic Symmetry by Self-conflicting Agents: Descriptive vs. Prescriptive Rules, In: proc of IEEE International Conference Integration of Knowledge Intensive Multi-Agent Systems, KIMAS '07, April 29-May 3, 2007, Waltham, Massachusetts
117. Kovalerchuk, B., Structural and metric correlation of electro-optical and radar generated tracks, SPIE Proc. Vol. 6565-8, April 9-12, 2007.
118. Kovalerchuk, B., Affine invariant and robust image registration/conflation algorithm, SPIE Defense and Security Symposium, Orlando, FL, Proc, Vol., 6568, 6568-05,2007, April 9-12.
119. Galitsky B., Kovalerchuk, B., Learning interaction between conflicting human agents and their assistants, AAAI 2007 Spring Symposium Interaction Challenges for Intelligent Assistants, 26-28 March 2007, Stanford University, CA, USA, AAAI Press, TR SS-07-04,
120. Kovalerchuk, B., Lemley, J., Gorbach, A. , An algorithm to stabilize a sequence of thermal brain images, SPIE Medical Imaging Conference, San Diego, Feb, 17-22, 2007, Proc. SPIE Vol. 6512, 6512-10, 2007.
121. Resconi, G., Kovalerchuk, B., Explanatory model for the break of logic equivalence by irrational agents in Elkan’s Paradox, Eleventh International Conference on Computer Aided Systems Theory, EUROCAST, Feb 12-16, 2007, Las Palmas, Spain.
2006
122.
Kovalerchuk B., (Editor), Proc. of the Second IASTED
International Conference on Computational Intelligence, CI 2006, November
20-22, 2006, San Francisco, California, USA.
123. Galitsky B., Kovalerchuk, B., Mining the attitudes of conflicting human agents. In: Proc. of the Second IASTED International Conference on Computational Intelligence, CI 2006, November 20-22, 2006, San Francisco, California, USA.
124. Resconi, G., Kovalerchuk, B., The Logic of Uncertainty with Irrational Agents, Joint International Information Science Conference, Taiwan, Oct. 2006.Atlantis Press, ISBN: 978-90-78677-01-7.
125. Galitsky B., Kovalerchuk, B., Analyzing attitude in customer emails: a tool for complaint assessment, Proceedings of the SIGIR 2006 Workshop on Directions in Computational Analysis of Stylistics in Text Retrieval, Part of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Seattle, WA.,, USA, August 10, 2006, pp. 17-36.
126. Kovalerchuk, B., Kamatkova, Yu., and Doucette, P., Overcoming the combined effect of geometric distortion and object change in image registration and conflation, Proc. SPIE Vol. 6233-Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery, 62331S (May. 4, 2006)
127. Kovalerchuk. B., Kovalerchuk, M., Sumner W., Robust and affine Invariant Image Fusion: Algorithm and ARCGIS Plug-In, In Proc. of ASPRS annual conference, 2006, Reno, NE, May 2006.
128. Galitsky B., Kovalerchuk, B., Mining the blogosphere for contributor`s sentiments, In Proc. of AAAI 2006 Spring Symposium on Computational Approaches to Analyzing Weblogs, March 27-29, 2006, Stanford University, CA, USA.
2005
129. B. Kovalerchuk, M. Kovalerchuk, W. Sumner, and A. Haase, Image Conflation and Change Detection Using Area Ratios, SPIE Defense and Security Symposium, Orlando, FL, 28 March-1April 2005
130. B. Kovalerchuk and R. Chase, Image Analysis Method for Overcoming Source Distortion Using Algebraic Invariant Methods, In: SPIE Proceedings Vol. 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, Sylvia S. Shen; Paul E. Lewis, Editors, 2005, pp.371-381.
131. B. Kovalerchuk, M. Kovalerchuk, W. Sumner, and A. Haase, Image Conflation and Change Detection Using Area Ratios, In: SPIE Defense and Security Symposium, Orlando, FL, 28 March- 1April 2005,
132. M. Kovalerchuk and B. Kovalerchuk: ASPRS 2005, Algorithm for Image Integration Invariant to Disproportional Scaling, In: ASPRS Annual Conference, Baltimore, Maryland March 7-11, 2005
133. B. Kovalerchuk , W. Sumner, and R. Chase , "Analysis of Vector and Raster Image Integration with Disproportional Scaling", ASPRS 2005 Annual Conference Baltimore, Maryland March 7-11, 2005
134. B. Kovalerchuk, A. Harper, and M. Kovalerchuk, Design of Virtual Experts for Imagery Registration and Conflation, ASPRS 2005 Annual Conference Baltimore, Maryland March 7-11, 2005
135. G. He, B. Kovalerchuk, and T. Mroz, "Multilevel Analytical and Visual Decision Framework for Imagery Registration and Conflation", ASPRS 2005 Annual Conference Baltimore, Maryland March 7-11, 2005
136. Vityaev, E., Kovalerchuk, B., Relational Methodology for Data Mining and Knowledge Discovery. DEXA Workshops 2005, Copenhagen, pp. 725-729.
2004
137. B. Kovalerchuk , W. Sumner, M. Curtiss, M. Kovalerchuk, and R. Chase, Matching Image Feature Structures Using Shoulder Analysis Method, In: Algorithms and technologies for multispectral, hyperspectral and ultraspectral imagery IX. Vol. 5425, International SPIE Military and Aerospace Symposium, AEROSENSE, Orlando, FL, April 12-15, 2004, pp. 508-519
138. E. Vityaev, B. Kovalerchuk, Empirical Theories Discovery based on the Measurement Theory. Mind and Machine, v.14, #4, 551-573, 2004
139. Kovalerchuk, B., Vityaev E., Data mining in finance: from extremes to realism, Journal of Financial Transformation, 2004,
140. Andonie, R. Kovalerchuk, B. Neural Networks for Data Mining: Constrains and Open Problems, Proceedings of the 12th European Symposium on Artificial Neural Networks (ESANN 2004), M. Verleysen (ed.), Bruges, Belgium, April 28-30, 2004, 449-458.
2003
141. Kovalerchuk, B., Context Space as a link between probability and non-classic uncertainty reasoning. In: Probabilistic ideas in science and philosophy, Conf. proc., Russian Academy of Sciences, Novosibirsk, Russia, Sept. 2003, pp. 95-99.
142. Kovalerchuk B., Sumner W., Algebraic relational approach to conflating images, In: Algorithms and technologies for multispectral, hyperspectral and ultraspectral imagery IX. Vol. 5093, International SPIE military and aerospace symposium, AEROSENSE, Orlando, FL., April 21-25, 2003, pp. 621-630.
143. Chase R., Kovalerchuk B., Computational Efficiency of Structural Image Matching In: Proceedings of International Conference on Imaging Science, Systems, and Technology (CISST'2003, June 23-26), Las Vegas, pp. 253-259, 2003.
144. Kovalerchuk B., Harper, A., Kovalerchuk, M., Virtual Experts for Imagery Registration and Conflation. In: Proceedings of International Conference on Imaging Science, Systems, and Technology (CISST'2003, June 23-26, Las Vegas, pp. 260-266, 2003.
145. Delizy F., Kovalerchuk B., Visual data Mining using monotone Boolean functions. In: Proceedings of International Conference on Imaging Science, Systems, and Technology (CISST'2003, June 23-26), Las Vegas, pp. 267-273, 2003.
146. Kovalerchuk B., Sumner W., Schwing J., Image registration and conflation based on structural characteristics, In Proceedings of International Conference on Imaging Science, Systems, and Technology (CISST'2003, June 23-26, 2002), Las Vegas, pp. 239-245, 2003.
147. Kovalerchuk, B., Vityaev, E., Detecting patterns of fraudulent behavior in forensic accounting, In Proc. of the Seventh International Conference Knowledge-based Intelligent Information and Engineering on Systems, Oxford, UK, Sept, 2003, part 1, pp. 502-509.
2002
148. Kovalerchuk, B., Relational Text Mining and Visualization, In: Knowledge-Based Intelligent Information Engineering Systems and Allied Technologies, KES 2002. Eds. E. Damiani, R. Howlett, L. Jain, N. Ichalkaranje, IOS Press, Amsterdam, 2002, part 2, pp. 1549-1554.
149. Kovalerchuk, B., Schwing J., Algebraic relational approach for geospatial feature correlation, In: Proceedings of International Conference on Imaging Science, Systems, and Technology (CISST'2002, June 24-27, 2002), Las Vegas, pp. 115-121, 2002.
150. Vityaev, E., Kovalerchuk, B. Inverse Visualization in Data Mining In: Proceedings of the International Conference on Imaging Science, Systems, and Technology, Eds.: H. R. Arabnia, Youngsong Mun, Las Vegas, Nevada, USA, June 24-27, 2002, CSREA Press, v.1, 133-138
151. Vityaev E.E., OrlovYu.L., Vishnevsky O.V., Kovalerchuk B.Ya., Belenok A.S., Podkolodnii N.L., Kolchanov N.A. Knowledge Discovery for Gene Regulatory Regions Analysis, In: Knowledge-Based Intelligent Information Engineering Systems and Allied Technologies, KES 2002. Eds. E.Damiani, R. Howlett, L.Jain, N. Ichalkaranje, IOS Press, Amsterdam, 2002, part 1, pp. 487-491.
2001
152. Kovalerchuk, B., Visualization and Decision-Making using Structural Information, In: Proceedings of International Conference on Imaging Science, Systems, and Technology (CISST'2001, June 25-28, 2001), Las Vegas, pp. 478-484.
153. Kovalerchuk, B., Schwing J., Mathematical support for combining geospatial data, In: Proceedings of International Conference on Imaging Science, Systems, and Technology (CISST'2001, June 25-28, 2001), Las Vegas, pp. 485-491.
154. Vityaev E.E., OrlovYu.L., Vishnevsky O.V., Kovalerchuk B.Ya., Belenok A.S., Podkolodnii N.L., Kolchanov N.A. Computer system "Gene Discovery" for functional annotation of DNA sequences. In: ECML'2001 Workshop Machine Learning as Philosophy of Science, Eds. K.B. Korb, H. Bensusan, Freiburg, Sept., 2001. p. 2-11.
155. Bichindaritz I., Kovalerchuk B., An Image Analysis Memory for Imagery Decision Support, CISST , Las Vegas, NV, 2001, pp. 473-477.
2000
156. Kovalerchuk, B., Book review: Safe and Sound, AI in hazardous applications by John Fox and Subrata Das, MIT Press, 2000 MIT Press. IEEE Engineering in Medicine and Biology, v. 20. n. 1, 2001, pp. 136-137.
157. Kovalerchuk B., Vityaev E., Ruiz J. Consistent knowledge discovery in medical diagnosis, IEEE Engineering in Medicine and Biology, (Special issue on Data Mining and Knowledge Discovery), v. 19, n. 4, pp. 26-37, 2000.
158. Kovalerchuk B., Triantaphyllou E., Ruiz J., Torvik, V., Vityaev, E. The reliability issue of Computer-Aided Breast cancer Diagnosis, Computers and Biomedical Research, v. 33, pp. 296-313, 2000. Abstract
159. Kovalerchuk, B., Todd C., Henderson D. Testing Neural Networks Using the Complete Round Robin Method, Proc. 5th Joint Conference on Information Sciences, February 27-March 3, 2000, Atlantic City, NJ, Association of Intelligent Machinery, vol. 1, pp.823-827, 2000. Short version. Abstract. Presentation ppt file.
1999
160. Kovalerchuk, B., Vityaev E., Comparison of relational methods and attribute-based methods for data mining in intelligent systems, 1999 IEEE Int. Symposium on Intelligent Control/Intelligent Systems, Cambridge, Mass, 1999. pp. 162-166. Working version: Comparison of relational methods, attribute-based methods and hybrid methods in Data Mining
1998
161. Kovalerchuk, B., Vityaev E. Discovering Law-like Regularities in Financial Time Series, Journal of Computational Intelligence in Finance, Vol.6, No.3, pp.12-26, 1998.
162. Kovalerchuk B., Vityaev E. Inductive Logic Programming for Discovering Financial Regularities, Workshop Data Mining in Finance, KDD 98, NY, 1998.
163. Kovalerchuk B., Conner N., Ruiz J., Clayton J. Fuzzy logic for formalization of breast imaging lexicon and feature extraction. 4th Workshop on Digital Mammography, Nijmegen, Netherlands, 1998.
164. Kovalerchuk, B., Ruiz J.F., Vityaev E., Fisher S. Prototype Internet consultation system for radiologists Journal of Digital Imaging, vol. 11, n 3, Suppl., 1998, pp.22-26. Abstract.
165. Kovalerchuk B., Kovalerchuk N. Object-Oriented Design and Multiresolution in Intelligent Control. Proceedings of the 1998 IEEE International Symposium on Intelligent Control (ISIC), IEEE, 1998, pp.120-125.
1997
166. Kovalerchuk, B., Triantaphyllou, E., Ruiz J., Clayton J. Fuzzy Logic in Computer-Aided Breast Cancer Diagnosis: Analysis of Lobulation, Artificial Intelligence in Medicine, No. 11, pp. 75-85, 1997.
167. Kovalerchuk, B., Vityaev, E., Ruiz J. Design of Consistent System for Radiologists to Support Breast Cancer Diagnosis. Proc. of Joint International Conf. of Information Sciences, March 1-5, 1997, Research Triangle Park, NC, Duke University, 1997, Vol. 2, pp.118-121. Presentation ppt. Presentation html.
168. Triantaphyllou, E., Kovalerchuk, B., Mann, L., Knapp G., Determining the most important criteria in maintenance decision making, Journal of quality in maintenance engineering, v. 3, n.1,1997, pp. 16-28.
1996
169. Kovalerchuk, B. Context spaces as necessary frames for correct approximate reasoning. International Journal of General Systems, v.25, n 1, 1996, pp. 61-80.
170. Kovalerchuk, B., Vityaev, E., Triantaphyllou, E. How can AI procedures become more effective for Manufacturing? In: Proc. of Workshop "Artificial Intelligence and Manufacturing", Sandia National Laboratories, AAAI Press, 1996, pp. 103-111.
171. Vityaev, E., Kovalerchuk, B., Logical data analysis of structural objects. The Fourth International Conference on Computational Biology: Intelligent Systems for Molecular Biology '96, June 12-15, 1996, St. Louis, Missouri.
172. Kovalerchuk, B., Triantaphyllou, E., Despande, A.S., and Vityaev, E. Interactive Learning of Monotone Boolean Functions, Information Sciences, Vol. 94, issue 1 4, 1996, pp. 87 118.
173. Kovalerchuk, B., Triantaphyllou, E., Ruiz J.F. and Clayton, J. Fuzzy logic in digital mammography: analysis of lobulation, Fifth IEEE International Conference on Fuzzy Systems, New Orleans, September 1996, pp. 1726-1731. Abstract.
174. Kovalerchuk, B. Second interpolation for fuzzy control, Fifth IEEE International Conference on Fuzzy Systems, New Orleans, September 1996, pp.150-155.
1995 and before
175. Kovalerchuk, B., Triantaphyllou, E., Vityaev, E. Monotone Boolean Function Learning Technique Integrated with user interaction. In: Proc. of 12th International conf. on Machine Learning, Workshop "Learning from Examples", Tahoe City, USA, 1995, pp. 41 48. Postscript, Abstract. Short version
176. Kovalerchuk, B., Klir, G. Linguistic context spaces and modal logic for approximate reasoning and fuzzy probability comparison. In: Proc. of Third International Symposium on Uncertainty Modeling and Analysis and NAFIPS' 95, College Park, Maryland, 1995, IEEE Computer Society Press, 1995, pp.A23 A28. Abstract
177. Kovalerchuk, B., Advantages of Exact Complete Context for Fuzzy Control. International Joint Conference on Information Science, Duke University (USA), November 13-16, 1994, pp.448-449.
178. Kovalerchuk, B., Spaces of Linguistic Contexts: Concepts and Examples. Second European Congress on Intelligent Techniques and Soft Computing, Aachen, Germany, September 20-23, 1994, pp. 345-349.
179. Kovalerchuk, B., et al. Comparison of interpolations in fuzzy control. 2nd IFAC Workshop on Computer Software Structures Integrating AI/KBS Systems in Process Control, Lund, Sweden,1994, pp. 76-81.
180. Kovalerchuk, B., Interpretation for Fuzzy Rules in Expert Systems and Control Systems. First Workshop on Fuzzy Based Expert Systems, Sofia, Bulgaria,1994, pp.81-82.
181. Kovalerchuk, B., Berezin, S., Context for definition of fuzzy operations. First European Congress on Fuzzy and Intelligent Technologies, Aachen, 1993, pp. 1531-1536.
182. Kovalerchuk, B., Talianski, V. Comparison of empirical and computed values of fuzzy conjunction. Fuzzy sets and systems, v. 46, 1992, pp. 49 53
183. Kovalerchuk, B. Yusupov, H., Why are believe functions effective? Consideration of examples. Cybernetics and Systems Research'92, Vol 1, World Scientific, 1992, pp.511-516.
184. Kovalerchuk, B., Shapiro, D., On the relation of the probability theory and the fuzzy sets foundations. Computers and Artificial Intelligence, v.7, 1988, pp. 385-396.