Universidade Federal do Rio de Janeiro COPPE

Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia

Instituto de Matemática

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Evento: 'Palestra do Prof. Alberto Fernandez Oliva, Universidad de Habana, Cuba'

Eventos PESC (Palestras, Seminários, etc.)
Palestras, Seminários, etc. do PESC/COPPE/UFRJ.
Data: Wednesday, February 25, 2015 At 11:00
Duração: 1 Dia

"Outlier detection based on the Variable Precision Rough Sets Model (VPRSM)"
(vide Resumo ao final).

Prof. Alberto Fernandez Oliva.
Universidad de Habana - Cuba

Data: 25 fevereiro 2015, Quarta-feira, 11:00 horas
Local: PESC/COPPE/UFRJ - Ilha Fundão - CT - H324 Anfiteatro.

In recent years, much attention has been paid to the problem of detecting outliers. In Data Mining processes, an outlier is an element of the universe that is being analyzed, that shows an abnormal behavior or that has unexpected values of the parameters taken into account. Different mathematical tools have been used to characterize an outlier: Statistical methods, Computational Geometry, Topological distances and Machine-Learning methods. A new approach to this problem (the mathematical definition of what be an outlier means and how to uncover them from a given dataset) is based on Rough Sets Theory. This approach was first proposed by Jiang et al. [1]. Coming from this characterization, we have carried out a set of lemmas and theorems. On this basis, we have developed an outlier detection Algorithm, further modified in terms of Variable Precision Model [2], in order to improve its applicability.

[1] F. Jiang, Y. Sui, and C. Cao, “Outlier detection based on rough membership function,” in Rough Sets and Current Trends in
Computing, 5th International Conference, RSCTC 2006, S. Greco, Y. Hata, S. Hirano, M. Inuiguchi, S. Miyamoto, H. S. Nguyen,
and R. Slowinski, Eds. Kobe, Japan: Springer, 2006, pp. 388–397.
[2]W. Ziarko, “Variable precision rough set model,” J. Comput. Syst. Sci., vol. 46, no. 1, pp. 39–59, 1993.

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