Abstract
This monograph presents theory concerning interval-valued fuzzy calculus, especially aggregation operators among which there are placed recently introduced pos-aggregation functions and nec-aggregation functions. Moreover, applications of interval-valued fuzzy methods (and more generally interval modeling), especially mentioned so far interval-valued aggregation functions in classification are provided. The presented algorithms may support decision processes for example in medical diagnosis. It was shown that applying interval-valued methods make it possible to improve classification results in situation when there is a large number of missing values or there is a large number of attributes in the considered data sets. The mentioned methods were applied in a few experiments, involving machine learning methods, whose results are presented in this monograph. The obtained results of the described algorithms confirm their usefulness. They allow effective classification in the case of uncertainty (imprecise or incomplete information). The source codes of the presented classification algorithms (from Chaps. 4 and 5) are available at [1].
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Bentkowska, U.: New types of aggregation functions for interval-valued fuzzy setting and preservation of pos-B and nec-B-transitivity in decision making problems. Inf. Sci. 424, 385–399 (2018)
Luengo, J., Herrera, F.: An automatic extraction method of the domains of competence for learning classifiers using data complexity measures. Knowl. Inf. Syst. 42, 147–180 (2015)
Lucca, G., Sanz, J.A., Dimuro, G.P., Bedregal, B., Bustince, H.: Analyzing the behavior of aggregation and pre-aggregation functions in fuzzy rule-based classification systems with data complexity measures. In: Kacprzyk, J., et al. (eds.) Advances in Intelligent Systems and Computing, vol. 642, pp. 443–455. Springer AG, Cham (2018)
Beliakov, G.: How to build aggregation operators from data. Int. J. Intell. Syst. 18, 903–923 (2003)
Beliakov, G., Gomez, D., James, J., Montero, J., Rodriguez, J.T.: Approaches to learning strictly-stable weights for data with missing values. Fuzzy Sets Syst. 325, 97–113 (2017)
Bartoszuk, M., Beliakov, G., Gagolewski, M., James, S.: Fitting aggregation functions to data: part I linearization and regularization. In: Carvalho, J.P., et al. (eds.) Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part II, CCIS 611, IPMU 2016, Eindhoven, Netherlands, 2016, pp. 767–779. Springer International Publishing, Switzerland (2016)
Bartoszuk, M., Beliakov, G., Gagolewski, M., James, S.: Fitting aggregation functions to data: part II idempotization. In: Carvalho, J.P., et al. (eds.) Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part II, CCIS 611, IPMU 2016, Eindhoven, Netherlands, 2016, pp. 780–789. Springer International Publishing, Switzerland (2016)
Beliakov, G.: Construction of aggregation functions from data using linear programming. Fuzzy Sets Syst. 160, 65–75 (2009)
Kaymak, U. Van Nauta Lemke, H.R.: Selecting an aggregation operator for fuzzy decision making. In: Proceedings of the 1994 IEEE 3rd International Fuzzy Systems Conference, vol. 2, pp. 1418–1422. Orlando, FL (1994)
Beliakov, G., Calvo, T.: Constructions of aggregation operators that preserve ordering of the data. In: Štěpnička, M., Novák, V., Bodenhofer, U. (eds.) New Dimensions in Fuzzy Logic and Related Technologies. Proceedings of the 5th EUSFLAT Conference, Ostrava, Czech Republic, 11–14 Sept 2007, pp. 61–66. Universitas Ostraviensis, Ostrava (2007)
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Bentkowska, U. (2020). Summary. In: Interval-Valued Methods in Classifications and Decisions. Studies in Fuzziness and Soft Computing, vol 378. Springer, Cham. https://doi.org/10.1007/978-3-030-12927-9_7
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DOI: https://doi.org/10.1007/978-3-030-12927-9_7
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