Abstract
The research is devoted to the solution of the scientific and practical problem of creation of intelligent systems (IS) of medical and sociological monitoring (MSM) on the basis of the improved models of representation of heterogeneous MSM’s data and the developed methods of their classification by means of Kohonen network training. In the research improve the models of representation of weakly structured and weakly labeled heterogeneous MSM’s data in the spaces of properties and features, taking into account values, types, formats, sources, quality assessments and procedures of aggregation/transformation of properties of detailed data. Based on these models, a method of matching classes and clusters markers in learning of Kohonen network with partial teacher involvement was developed, which is based on constructing a two-dimensional histogram of pairwise matches of classes and clusters markers values with its subsequent intersecting by rows and by columns to the developed rule. The method allows to obtain additional class markers in the unlabeled part of the training sample. Based on the method of matching classes and clusters markers to assess the suitability of chromosomes in the population of the genetic algorithm for each example from the training sample, a method of heuristic weight adjustment in the learning process of the Kohonen neural network is proposed. The use of such an adjustment of the weights allowed to reduce the training time of the Kohonen net-work without losing the level of reliability of the classification. The method of classification of weakly structured and weakly labeled heterogeneous MSM’s data has been improved due to the use of developed methods of matching class and cluster markers and heuristic adjustment in the process of learning the Kohonen network
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Change history
14 September 2022
A correction has been published.
References
Sociological survey data “Ukraine - life style”. http://edukacjainauka.pl/limesurvey/index.php/lang-pl
Alzubaidi, L., et al.: Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. Big Data 8(53) (2021). https://doi.org/10.1186/s40537-021-00444-8
Arsirii, O., Antoshchuk, S., Babilunha, O., Manikaeva, O., Nikolenko, A.: Intellectual information technology of analysis of weakly-structured multi-dimensional data of sociological research. Adv. Intell. Syst. Comput. 1020, 242–258 (2020). https://doi.org/10.1007/978-3-030-26474-1_18
Arsirii, O., Babilunha, O., Manikaeva, O., Rudenko, O.: Automation of the preparation process weakly-structured multi-dimensional data of sociological surveys in the data mining system. Herald Adv. Inf. Technol. 1(1), 9–17 (2018). https://doi.org/10.15276/hait.01.2018.1
Arsirii, O., Manikaeva, O.: Models and methods of intellectual analysis for medical-sociological monitoring data based on the neural network with a competitive layer. Appl. Aspects Inf. Technol. 2(3), 173–185 (2019). https://doi.org/10.15276/aait.03.2019.1
Manikaeva, O., Arsirii, E., Vasilevskaja, A.: Development of the decision support subsystem in the systems of neural network pattern recognition by statistical information. East. Eur. J. Enterp. Technol. 6(4(78)), 4–12 (2015). https://doi.org/10.15587/1729-4061.2015.56429
Arulkumaran, K., Deisenroth, M.P., Brundage, M., Bharath, A.A.: Deep reinforcement learning: a brief survey. IEEE Signal Process. Mag. 34, 26–38 (2017). https://doi.org/10.1109/MSP.2017.2743240
Barsegyan, A.A., Kupriyanov, M.S., Stepanenko, V.V., Holod, I.I.: Metodyi i modeli analiza dannyih: OLAP i Data Mining. BHV-Peterburg, SPb (2004)
Belciug, S., Gorunescu, F.: Era of intelligent systems in healthcare. In: Intelligent Decision Support Systems—A Journey to Smarter Healthcare. ISRL, vol. 157, pp. 1–55. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-14354-1_1
Buja, A., Swayne, D.F., Littman, M.L., Dean, N., Hofmann, H., Chen, L.: Data visualization with multidimensional scaling. J. Comput. Graph. Stat. 17(2), 444–472 (2008). https://doi.org/10.1198/106186008X318440
Chapman, P., et al.: Crisp-dm 1.0: step-by-step data mining guide (2000)
Corbetta, P.: Social Research: Theory, Methods and Techniques. Sage, London (2011). https://doi.org/10.4135/9781849209922
Djouzi, K., Beghdad-Bey, K.: A review of clustering algorithms for big data. In: 2019 International Conference on Networking and Advanced Systems (ICNAS), pp. 1–6 (2019). https://doi.org/10.1109/ICNAS.2019.8807822
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in data bases. AI Mag. 17(3), 37–54 (1996). https://doi.org/10.1609/aimag.v17i3.1230
Härdle, W., Simar, L.: Applied Multivariate Statistical Analysis Free preview. Springer, New York (2012). https://doi.org/10.1007/978-3-030-26006-4
Hartigan, J., Wong, M.: Algorithm AS 13: a k-means clustering algorithm. Appl. Stat. 28(1), 100–108 (1979). https://doi.org/10.2307/2346830
Haykin, S.: Neural Networks and Learning Machines, 3rd edn. McMaster University, Ontario (2009)
Katoch, S., Chauhan, S., Kumar, V.: A review on genetic algorithm: past, present, and future. Multimedia Tools Appl. 80(5), 8091–8126 (2021). https://doi.org/10.1007/s11042-020-10139-6
Kohonen, T.: The self-organizing map. Proc. IEEE 78(9), 1464–1480 (1990). https://doi.org/10.1109/5.58325
Merkert, J., Müller, M., Hubl, M.: A survey of the application of machine learning in decision support (2015). https://doi.org/10.18151/7217429
Praveen, S., Chandra, U.: Influence of structured, semi-structured, unstructured data on various data models. Int. J. Sci. Eng. Res. 8(12), 67–69 (2017)
Qin, L., et al.: Prediction of number of cases of 2019 novel coronavirus (Covid-19) using social media search index. Int. J. Environ. Res. Public Health 17(7) (2020). https://doi.org/10.3390/ijerph17072365
Rudenko, A.I., Arsirii, E.A.: Metodika intellektualnogo analiza slabostrukturirovannyih mnogomernyih dannyih sotsiologicheskih oprosov [Methods of intellectual analysis of poorly structured multidimensional data of sociological surveys]. In: Modern Information Technology 2018, pp. 168–169. MON Ukrayini, Odes. Nats. politeh. un-t; In-t komp’yut. sistem, Odesa, Ekologiya (2018)
Tan, F.B., Hunter, G.M.: The repertory grid technique: a method for the study of cognition in information systems. MIS Q. 26(1), 39–57 (2002). https://doi.org/10.2307/4132340
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Arsirii, O., Antoshchuk, S., Manikaeva, O., Babilunha, O., Nikolenko, A. (2023). Classification Methods of Heterogeneous Data in Intellectual Systems of Medical and Social Monitoring. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making. ISDMCI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 149. Springer, Cham. https://doi.org/10.1007/978-3-031-16203-9_38
Download citation
DOI: https://doi.org/10.1007/978-3-031-16203-9_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16202-2
Online ISBN: 978-3-031-16203-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)