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.pl
Lublin 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 data

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

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Published
2021-09-30

Cited by

Kiersztyn, A., Gandzel, A., Celiński, M., & Koczan, L. (2021). GRANULAR REPRESENTATION OF THE INFORMATION POTENTIAL OF VARIABLES - APPLICATION EXAMPLE. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 11(3), 40–44. https://doi.org/10.35784/iapgos.2700

Authors

Adam Kiersztyn 

Lublin University of Technology , Department of Computer Science Poland
http://orcid.org/0000-0001-5222-8101

Authors

Agnieszka Gandzel 

Lublin University of Technology, Faculty od Technology Fundamentals Poland
http://orcid.org/0000-0002-7887-8636

Authors

Maciej Celiński 
m.celinski@pollub.pl
Lublin University of Technology, Faculty od Technology Fundamentals Poland
http://orcid.org/0000-0001-8412-207X

Authors

Leopold Koczan 

Lublin University of Technology, Faculty od Technology Fundamentals Poland
http://orcid.org/0000-0002-7775-1836

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