Combining classifiers for foreign pattern rejectionCombining classifiers for foreign pattern rejection

• Autorzy: Homenda Władysław , Jastrzębska Agnieszka , Pedrycz Witold , Yu Fusheng

Identyfikatory

DOI

10.2478/jaiscr-2020-0006

• Języki publikacji: EN

Abstrakty

EN

In this paper, we look closely at the issue of contaminated data sets, where apart from legitimate (proper) patterns we encounter erroneous patterns. In a typical scenario, the classification of a contaminated data set is always negatively influenced by garbage patterns (referred to as foreign patterns). Ideally, we would like to remove them from the data set entirely. The paper is devoted to comparison and analysis of three different models capable to perform classification of proper patterns with rejection of foreign patterns. It should be stressed that the studied models are constructed using proper patterns only, and no knowledge about thecharacteristics of foreign patterns is needed. The methods are illustrated with a case study of handwritten digits recognition, but the proposed approach itself is formulated in a general manner. Therefore, it can be applied to different problems. We have distinguished three structures: global, local, and embedded, all capable to eliminate foreign patterns while performing classification of proper patterns at the same time. A comparison of the proposed models shows that the embedded structure provides the best results but at the cost of a relatively high model complexity. The local architecture provides satisfying results and at the same time is relatively simple.

Słowa kluczowe

EN

data mining; knowledge engineering

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2020
• Tom: Vol. 10, No. 2
• Strony: 75--94
• Opis fizyczny: Bibliogr. 27 poz., rys.

Twórcy

autor

Homenda Władysław

• The Faculty of Mathematics and Information Science Warsaw University of Technology, Poland
• The Faculty of Economics and Informatics in Vilnius University of Bialystok, Vilnius, Lithuania

autor

Jastrzębska Agnieszka

• The Faculty of Mathematics and Information Science Warsaw University of Technology, Poland
• The Systems Research Institute Polish Academy of Sciences, Warsaw, Poland

autor

Pedrycz Witold

• The University of Alberta, Edmonton, Canada
• The Systems Research Institute Polish Academy of Sciences, Warsaw, Poland

autor

Yu Fusheng

• The Beijing Normal University, Beijing, China

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).