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Non-Deterministic Outlier Detection Method Based on the Variable Precision Rough Set Model

Alberto Fernández Oliva1, Francisco Maciá Pérez2, José Vicente Berná-Martinez2,*, Miguel Abreu Ortega3

1 Department of Computer Science, Faculty of Mathematics and Computer Science, University of Havana, Cuba
2 Department of Computer Technology, University of Alicante, Spain
3 Department of E-Commerce, Carnival Cruise Line, Florida, United States

* Corresponding Author: E-mail: , Telephone +34 96 590 34 00 – ext. 2114, Fax: +34 96 590 9643

Computer Systems Science and Engineering 2019, 34(3), 131-144.


This study presents a method for the detection of outliers based on the Variable Precision Rough Set Model (VPRSM). The basis of this model is the generalisation of the standard concept of a set inclusion relation on which the Rough Set Basic Model (RSBM) is based. The primary contribution of this study is the improvement in detection quality, which is achieved due to the generalisation allowed by the classification system that allows a certain degree of uncertainty. From this method, a computationally efficient algorithm is proposed. The experiments performed with a real scenario and a comparison of the results with the RSBM-based method demonstrate the effectiveness of the method as well as the algorithm’s efficiency in diverse contexts, which also involve large amounts of data.


Cite This Article

A. Fernández Oliva, F. Maciá Pérez, J. Vicente Berná-Martinez and M. Abreu Ortega, "Non-deterministic outlier detection method based on the variable precision rough set model," Computer Systems Science and Engineering, vol. 34, no.3, pp. 131–144, 2019.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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