A multivariate fuzzy system applied for outliers detection

Article type: Research Article

Authors: Cateni, Silvia | Colla, Valentina | Nastasi, Gianluca

Affiliations: PERCRO Laboratory, Scuola Superiore S. Anna, Pisa Italy

Note: [] Corresponding author. Silvia Cateni, PERCRO laboratory, Scuola Superiore S. Anna, Pisa Italy. E-mail: [email protected] (Silvia Cateni); [email protected] (Valentina Colla); [email protected] (Gianluca Nastasi).

Abstract: The paper presents an application of fuzzy logic to the problem of outliers detection. The overall purpose of the work is to point out anomalous data due different causes through a combination of several traditional methods for outliers detection in multivariate datasets and such combination is achieved through a fuzzy inference system. Moreover, the proposed solutions aims to be automatic and self-adaptive, as some parameters which are required for the combination of the different approaches are automatically evaluated by exploiting the available data, without the need of a-priori assumptions or information on a subset of the available data. The proposed method therefore belongs to the class of the unsupervised outliers detection methods. In order to demonstrate the effectiveness of the developed method, extensive tests have been performed on both a simple case study and a database coming from a real industrial context, where the data have to be filtered before their exploitation for process control purposes. The achieved numerical results are presented and discussed.

Keywords: Outlier detection, fuzzy inference system

DOI: 10.3233/IFS-2012-0607

Journal: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 4, pp. 889-903, 2013