RSF: Roughset Theory Based Fuzzy Classification in Randomized Dimensionality Feature Selection

International Journal of Computer Science and Engineering
© 2019 by SSRG - IJCSE Journal
Volume 6 Issue 4
Year of Publication : 2019
Authors : D. Barathi

How to Cite?

D. Barathi, "RSF: Roughset Theory Based Fuzzy Classification in Randomized Dimensionality Feature Selection," SSRG International Journal of Computer Science and Engineering , vol. 6,  no. 4, pp. 25-28, 2019. Crossref,


Feature Selection is a pattern dimensionality using feature mining and feature selection fit in to the data mining. To improve the robustness of the feature selection algorithm and for visualization point the dimension reduction techniques may be analyzed. The randomized feature selection is the transformation of high-dimensional data into animportantdesign of reduced dimensionality that corresponds to the fundamental dimensionality of the data. The normal reduction algorithm often not well for large datasets and fault dimensionality reduction, hence, to enhance the efficiency, the proposed system apply Roughset theory based fuzzy classification on original data set and obtain a reduced dataset containing possibly uncorrelated variables. In this paper, Roughset theory for feature selection and fuzzy based classification for Feature selection non-linear conversion is used for reduce the dimensionality and primary crisp value is calculated, then it is applied to classification algorithm.


Rough set, Fuzzy, preprocessing.


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