17 March 2017 Fuzzy feature selection based on interval type-2 fuzzy sets
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Proceedings Volume 10341, Ninth International Conference on Machine Vision (ICMV 2016); 103412M (2017) https://doi.org/10.1117/12.2268796
Event: Ninth International Conference on Machine Vision, 2016, Nice, France
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.
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Sahar Cherif, Nesrine Baklouti, Adel Alimi, Vaclav Snasel, "Fuzzy feature selection based on interval type-2 fuzzy sets", Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103412M (17 March 2017); doi: 10.1117/12.2268796; https://doi.org/10.1117/12.2268796
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