Non-deterministic outlier detection method based on the variable precision rough set model

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/107890
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Title: Non-deterministic outlier detection method based on the variable precision rough set model
Authors: Fernández Oliva, Alberto | Maciá Pérez, Francisco | Berna-Martinez, Jose Vicente | Abreu Ortega, Miguel
Research Group/s: GrupoM. Redes y Middleware
Center, Department or Service: Universidad de Alicante. Departamento de Tecnología Informática y Computación
Keywords: Outliers | Rough Sets (RS) | RS Basic Model (RSBM) | Variable Precision Rough Set Model (VPRSM) | Data set | Data Mining
Knowledge Area: Arquitectura y Tecnología de Computadores
Issue Date: 2019
Publisher: CRL Publishing
Citation: International Journal of Computer Systems Science & Engineering. 2019, 3: 131-144
Abstract: 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.
Sponsor: This study was funded by grant TIN2016-78103-C2-2-R and University of Alicante GRE14-02.
URI: http://hdl.handle.net/10045/107890
ISSN: 0267-6192
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2019 CRL Publishing Ltd
Peer Review: si
Publisher version: http://crl-publishing.co.uk/csse-journal/
Appears in Collections:INV - GrupoM - Artículos de Revistas

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