GRANULAR REPRESENTATION OF THE INFORMATION POTENTIAL OF VARIABLES - APPLICATION EXAMPLE
Adam Kiersztyn
Lublin University of Technology , Department of Computer Science (Poland)
http://orcid.org/0000-0001-5222-8101
Agnieszka Gandzel
Lublin University of Technology, Faculty od Technology Fundamentals (Poland)
http://orcid.org/0000-0002-7887-8636
Maciej Celiński
m.celinski@pollub.plLublin University of Technology, Faculty od Technology Fundamentals (Poland)
http://orcid.org/0000-0001-8412-207X
Leopold Koczan
Lublin University of Technology, Faculty od Technology Fundamentals (Poland)
http://orcid.org/0000-0002-7775-1836
Abstract
With the introduction to the science paradigm of Granular Computing, in particular, information granules, the way of thinking about data has changed gradually. Both specialists and scientists stopped focusing on the single data records themselves, but began to look at the analyzed data in a broader context, closer to the way people think. This kind of knowledge representation is expressed, in particular, in approaches based on linguistic modelling or fuzzy techniques such as fuzzy clustering. Therefore, especially important from the point of view of the methodology of data research, is an attempt to understand their potential as information granules. In this study, we will present special cases of using the innovative method of representing the information potential of variables with the use of information granules. In a series of numerical experiments based on both artificially generated data and ecological data on changes in bird arrival dates in the context of climate change, we demonstrate the effectiveness of the proposed approach using classic, not fuzzy measures building information granules.
Keywords:
granular computing, information granules, knowledge representation, fuzzy clustering, ecological dataReferences
Altonji J. G., Elder T. E., Taber C. R.: Selection on observed and unobserved variables: Assessing the effectiveness of catholic schools. Journal of Political Economy 113(1), 2005, 151–184 [http://doi.org/10.1086/426036].
DOI: https://doi.org/10.1086/426036
Google Scholar
Barbieri M. M., Berger J. O.: Optimal predictive model selection. Ann. Statist. 32(3), 2004, 870–897 [http://doi.org/10.1214/009053604000000238].
DOI: https://doi.org/10.1214/009053604000000238
Google Scholar
Bargiela A., Pedrycz W.: Human-centric information processing through granular modelling. Springer Science & Business Media 182, 2009 [http://doi.org/10.1007/978-3-540-92916-1].
DOI: https://doi.org/10.1007/978-3-540-92916-1
Google Scholar
Bargiela A., Pedrycz W.: Granular computing. In: Handbook on Computational Intelligence. World Scientific, 2016 [http://doi.org/10.1142/9789814675017_0002].
DOI: https://doi.org/10.1142/9789814675017_0002
Google Scholar
Bursac Z., Gauss, C. H., Williams D. K., Hosmer D. W.: Purposeful selection of variables in logistic regression. Source Code for Biology and Medicine 3(1), 2008, 17 [http://doi.org/10.1186/1751-0473-3-17].
DOI: https://doi.org/10.1186/1751-0473-3-17
Google Scholar
Gauch H.: Model selection and validation for yield trials with interaction. Biometrics 44(3), 1988, 705–715 [http://doi.org/10.2307/2531585].
DOI: https://doi.org/10.2307/2531585
Google Scholar
Geisser S., Eddy W. F.: A predictive approach to model selection. Journal of the American Statistical Association 74(365), 1979, 153–160 [http://doi.org/10.1080/01621459.1979.10481632].
DOI: https://doi.org/10.1080/01621459.1979.10481632
Google Scholar
Genuer R., Poggi J. M., Tuleau-Malot C.: Variable selection using random forests. Pattern Recognition Letters 31(14), 2010, 2225–2236 [http://doi.org/10.1016/j.patrec.2010.03.014].
DOI: https://doi.org/10.1016/j.patrec.2010.03.014
Google Scholar
Johnson J. B., Omland K. S.: Model selection in ecology and evolution. Trends in Ecology & Evolution 19(2), 2004, 101–108 [http://doi.org/10.1016/j.tree.2003.10.013].
DOI: https://doi.org/10.1016/j.tree.2003.10.013
Google Scholar
Kiersztyn A., Karczmarek P., Lopucki R., Pedrycz W., Al E., Kitowski I., Zbyryt A.: Data imputation in related time series using fuzzy set-based techniques. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Glasgow 2020, 1–8.
DOI: https://doi.org/10.1109/FUZZ48607.2020.9177617
Google Scholar
Kiersztyn A., Karczmarek P., Kiersztyn K., Pedrycz W.: Detection and Classification of Anomalies in Large Data Sets on the Basis of Information Granules. IEEE Transactions on Fuzzy Systems, 2021 [htp://doi.org/10.1109/TFUZZ.2021.3076265].
DOI: https://doi.org/10.1109/FUZZ45933.2021.9494466
Google Scholar
Kiersztyn A., Karczmarek P., Kiersztyn K., Pedrycz W.: The Concept of Detecting and Classifying Anomalies in Large Data Sets on a Basis of Information Granules. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2020, 1–7.
DOI: https://doi.org/10.1109/TFUZZ.2021.3076265
Google Scholar
Kiersztyn A., Karczmarek P., Kiersztyn K., Łopucki R., Grzegórski S., Pedrycz W.: The Concept of Granular Representation of the Information Potential of Variables. 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2021, 1–6.
DOI: https://doi.org/10.1109/FUZZ45933.2021.9494582
Google Scholar
Laud P.W., Ibrahim J.G.: Predictive model selection. Journal of the Royal Statistical Society: Series B (Methodological) 57(1), 1995, 247–262 [http://doi.org/10.1111/j.2517-6161.1995.tb02028].
DOI: https://doi.org/10.1111/j.2517-6161.1995.tb02028.x
Google Scholar
Mac Nally R.: Regression and model-building in conservation biology, biogeography and ecology: the distinction between – and reconciliation of – "predictive" and "explanatory" models. Biodiversity & Conservation 9(5), 2000, 655–671 [http://doi.org/10.1023/A:1008985925162].
DOI: https://doi.org/10.1023/A:1008985925162
Google Scholar
Olivera A. R., Roesler V., Iochpe C., Schmidt M. I., Vigo A., Barreto S. M., Duncan B. B.: Comparison of machine-learning algorithms to build a predictive model for detecting undiagnosed diabetes-elsa-brasil: Accuracy study. Sao Paulo Medical Journal 135(3), 2017, 234–246 [http://doi.org/10.1590/1516-3180.2016.0309010217].
DOI: https://doi.org/10.1590/1516-3180.2016.0309010217
Google Scholar
Pearce-Higgins J. W., Green R. E.: Birds and climate change: Impacts and conservation responses. Cambridge University Press 2014.
DOI: https://doi.org/10.1017/CBO9781139047791
Google Scholar
Pedrycz W.: Knowledge-based clustering: From data to information granules. John Wiley & Sons, 2005 [http://doi.org/10.5555/1044924].
DOI: https://doi.org/10.1002/0471708607
Google Scholar
Piironen J., Vehtari A.: Projection predictive model selection for Gaussian processes. IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), Salerno 2016, 1–6.
DOI: https://doi.org/10.1109/MLSP.2016.7738829
Google Scholar
Piironen J., Vehtari A.: Comparison of Bayesian predictive methods for model selection. Statistics and Computing 27(3), 2017, 711–735. [http://doi.org/10.1007/s11222-016-9649-y].
DOI: https://doi.org/10.1007/s11222-016-9649-y
Google Scholar
ptop.org.pl (2016), (available: 01.10.2020).
Google Scholar
Schafer B. C., Wakabayashi K.: Machine learning predictive modelling high-level synthesis design space exploration. IET Computers & Digital Techniques 6(3), 2012, 153–159 [http://doi.org/10.1049/iet-cdt.2011.0115].
DOI: https://doi.org/10.1049/iet-cdt.2011.0115
Google Scholar
Smith A., Naik P. A., Tsai C. L.: Markov-switching model selection using Kullback-Leibler divergence. Journal of Econometrics 134(2), 2006, 553–577 [http://doi.org/10.1016/j.jeconom.2005.07.005].
DOI: https://doi.org/10.1016/j.jeconom.2005.07.005
Google Scholar
Stephens P. A., Mason L. R., Green R. E., Gregory R. D., Sauer J. R., Alison J., Aunins A., Brotons L., Butchart S. H., Campedelli T., et al.: Consistent response of bird populations to climate change on two continents. Science 352(6281), 2016, 84–87 [http://doi.org/10.1126/science.aac4858].
DOI: https://doi.org/10.1126/science.aac4858
Google Scholar
Symonds M. R., Moussalli A.: A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike's information criterion. Behavioral Ecology and Sociobiology 65(1), 2011, 13–21 [http://doi.org/10.1007/s00265-010-1037-6].
DOI: https://doi.org/10.1007/s00265-010-1037-6
Google Scholar
Authors
Adam KiersztynLublin University of Technology , Department of Computer Science Poland
http://orcid.org/0000-0001-5222-8101
Authors
Agnieszka GandzelLublin University of Technology, Faculty od Technology Fundamentals Poland
http://orcid.org/0000-0002-7887-8636
Authors
Maciej Celińskim.celinski@pollub.pl
Lublin University of Technology, Faculty od Technology Fundamentals Poland
http://orcid.org/0000-0001-8412-207X
Authors
Leopold KoczanLublin University of Technology, Faculty od Technology Fundamentals Poland
http://orcid.org/0000-0002-7775-1836
Statistics
Abstract views: 338PDF downloads: 161
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Most read articles by the same author(s)
- Bartosz Wijatkowski, Jakub Smołka, Maciej Celiński, INFLUENCE OF A PLATFORM GAME CONTROL METHOD ON A PLAYER’S EFFECTIVENESS , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 11 No. 3 (2021)
- Maciej Celiński, Adam Kiersztyn, REVIEW OF THE ACHIEVEMENTS OF EMPLOYEES OF THE LUBLIN UNIVERSITY OF TECHNOLOGY IN THE FIELD OF FUZZY SET UTILIZATION , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 14 No. 2 (2024)
