Rough membership and Bayesian confirmation measures for parameterized rough sets

Salvatore Greco; B. Matarazzo; R. Slowinski

doi:10.1007/11548669_33

Rough membership and Bayesian confirmation measures for parameterized rough sets

Salvatore Greco, B. Matarazzo, R. Slowinski

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

Abstract

A generalization of the original idea of rough sets and variable precision rough sets is introduced. This generalization is based on the concept of absolute and relative rough membership. Similarly to variable precision rough set model, the generalization called parameterized rough set model, is aimed at modeling data relationships expressed in terms of frequency distribution rather than in terms of a full inclusion relation used in the classical rough set approach. However, differently from variable precision rough set model, one or more parameters modeling the degree to which the condition attribute values confirm the decision attribute value, are considered. The properties of this extended model are investigated and compared to the classical rough set model and the variable precision rough set model.

Original language	English
Title of host publication	Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing: Proceedings of the 10th International Conference. Vol. 1
Editors	D. Slezak, G. Wang, M. Szczuka, I. Duntsch, Y. Yao
Place of Publication	Berlin
Publisher	Springer
Pages	314-324
Number of pages	11
ISBN (Print)	9783540286530
DOIs	https://doi.org/10.1007/11548669_33
Publication status	Published - Sept 2005

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Number	3641
ISSN (Print)	0302-9743

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http://link.springer.com/chapter/10.1007%2F11548669_33

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Greco, S., Matarazzo, B., & Slowinski, R. (2005). Rough membership and Bayesian confirmation measures for parameterized rough sets. In D. Slezak, G. Wang, M. Szczuka, I. Duntsch, & Y. Yao (Eds.), Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing: Proceedings of the 10th International Conference. Vol. 1 (pp. 314-324). (Lecture Notes in Computer Science; No. 3641). Springer. https://doi.org/10.1007/11548669_33

Greco, Salvatore ; Matarazzo, B. ; Slowinski, R. / Rough membership and Bayesian confirmation measures for parameterized rough sets. Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing: Proceedings of the 10th International Conference. Vol. 1. editor / D. Slezak ; G. Wang ; M. Szczuka ; I. Duntsch ; Y. Yao. Berlin : Springer, 2005. pp. 314-324 (Lecture Notes in Computer Science; 3641).

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abstract = "A generalization of the original idea of rough sets and variable precision rough sets is introduced. This generalization is based on the concept of absolute and relative rough membership. Similarly to variable precision rough set model, the generalization called parameterized rough set model, is aimed at modeling data relationships expressed in terms of frequency distribution rather than in terms of a full inclusion relation used in the classical rough set approach. However, differently from variable precision rough set model, one or more parameters modeling the degree to which the condition attribute values confirm the decision attribute value, are considered. The properties of this extended model are investigated and compared to the classical rough set model and the variable precision rough set model.",

author = "Salvatore Greco and B. Matarazzo and R. Slowinski",

year = "2005",

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series = "Lecture Notes in Computer Science",

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Greco, S, Matarazzo, B & Slowinski, R 2005, Rough membership and Bayesian confirmation measures for parameterized rough sets. in D Slezak, G Wang, M Szczuka, I Duntsch & Y Yao (eds), Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing: Proceedings of the 10th International Conference. Vol. 1. Lecture Notes in Computer Science, no. 3641, Springer, Berlin, pp. 314-324. https://doi.org/10.1007/11548669_33

Rough membership and Bayesian confirmation measures for parameterized rough sets. / Greco, Salvatore; Matarazzo, B.; Slowinski, R.
Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing: Proceedings of the 10th International Conference. Vol. 1. ed. / D. Slezak; G. Wang; M. Szczuka; I. Duntsch; Y. Yao. Berlin: Springer, 2005. p. 314-324 (Lecture Notes in Computer Science; No. 3641).

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

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N2 - A generalization of the original idea of rough sets and variable precision rough sets is introduced. This generalization is based on the concept of absolute and relative rough membership. Similarly to variable precision rough set model, the generalization called parameterized rough set model, is aimed at modeling data relationships expressed in terms of frequency distribution rather than in terms of a full inclusion relation used in the classical rough set approach. However, differently from variable precision rough set model, one or more parameters modeling the degree to which the condition attribute values confirm the decision attribute value, are considered. The properties of this extended model are investigated and compared to the classical rough set model and the variable precision rough set model.

AB - A generalization of the original idea of rough sets and variable precision rough sets is introduced. This generalization is based on the concept of absolute and relative rough membership. Similarly to variable precision rough set model, the generalization called parameterized rough set model, is aimed at modeling data relationships expressed in terms of frequency distribution rather than in terms of a full inclusion relation used in the classical rough set approach. However, differently from variable precision rough set model, one or more parameters modeling the degree to which the condition attribute values confirm the decision attribute value, are considered. The properties of this extended model are investigated and compared to the classical rough set model and the variable precision rough set model.

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M3 - Chapter (peer-reviewed)

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BT - Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing: Proceedings of the 10th International Conference. Vol. 1

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Greco S, Matarazzo B, Slowinski R. Rough membership and Bayesian confirmation measures for parameterized rough sets. In Slezak D, Wang G, Szczuka M, Duntsch I, Yao Y, editors, Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing: Proceedings of the 10th International Conference. Vol. 1. Berlin: Springer. 2005. p. 314-324. (Lecture Notes in Computer Science; 3641). doi: 10.1007/11548669_33

Rough membership and Bayesian confirmation measures for parameterized rough sets

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