Abstract
Natural language is always seen as a source of uncertainty and vagueness. Fuzzy logic (FL) is a powerful tool for representing and treating perceptions which are the inputs and outputs of a linguistic model. In fact, a linguistic representation is a methodology that moves from crisp measures to uncertain words or fuzzy concepts. This theory uses fuzzy sets to encode and represent linguistic concepts. In this paper, an interval type-2 fuzzy formal concept IT-2FFC is presented as a new approach for extracting knowledge in a linguistic model. The method represents a combination of two techniques: fuzzy formal concept (FFC) for visualizing data and interval type-2 fuzzy sets (IT-2FSs) for feature selection. The obtained results demonstrate that the method applied can help human to make subjective judgments and make decision in a knowledge model.
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Acknowledgment
The authors would like to acknowledge the financial support of this work by grants from General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program.
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Cherif, S., Baklouti, N., Alimi, A.M., Snasel, V. (2017). Linguistic Representation by Fuzzy Formal Concept and Interval Type-2 Feature Selection. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_105
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DOI: https://doi.org/10.1007/978-3-319-53480-0_105
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