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Proceedings Paper

Fuzzy feature selection based on interval type-2 fuzzy sets
Author(s): Sahar Cherif; Nesrine Baklouti; Adel Alimi; Vaclav Snasel
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Paper Abstract

When dealing with real world data; noise, complexity, dimensionality, uncertainty and irrelevance can lead to low performance and insignificant judgment. Fuzzy logic is a powerful tool for controlling conflicting attributes which can have similar effects and close meanings. In this paper, an interval type-2 fuzzy feature selection is presented as a new approach for removing irrelevant features and reducing complexity. We demonstrate how can Feature Selection be joined with Interval Type-2 Fuzzy Logic for keeping significant features and hence reducing time complexity. The proposed method is compared with some other approaches. The results show that the number of attributes is proportionally small.

Paper Details

Date Published: 17 March 2017
PDF: 5 pages
Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103412M (17 March 2017); doi: 10.1117/12.2268796
Show Author Affiliations
Sahar Cherif, Ecole Nationale d'Ingénieurs de Sfax (Tunisia)
Nesrine Baklouti, Ecole Nationale d'Ingénieurs de Sfax (Tunisia)
Adel Alimi, Ecole Nationale d'Ingénieurs de Sfax (Tunisia)
Vaclav Snasel, VŠB-Technical Univ. of Ostrava (Czech Republic)


Published in SPIE Proceedings Vol. 10341:
Ninth International Conference on Machine Vision (ICMV 2016)
Antanas Verikas; Petia Radeva; Dmitry P. Nikolaev; Wei Zhang; Jianhong Zhou, Editor(s)

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